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Speaker 1: Views and opinions expressed by the guests that Sasquatch Experience

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do not necessarily reflect the opinion of the host, sponsors,

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or affiliates of the Sasquatch Experience. As always, listener discretion

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is advised.

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Speaker 2: We got some one or something crawling around out here,

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and see what it was?

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Speaker 1: Was it a person or an animal.

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Speaker 2: Or I can't know. All I know that my underway

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came on and I get happened to Glenn and be

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the thing running across the head good bye man or something.

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Speaker 3: To look by the men.

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Speaker 2: I don't know what it was.

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Speaker 4: Back in two thousand and five, we set out with

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one goal to give voice to the mystery and those

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who pursue it. Sasquatch Experience podcast has been your go

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to source for serious Bigfoot discussion, where we separate fact

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from folklore. Always grounded in research and respect. We've interviewed eyewitnesses,

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experts since Skeptic to Like because understanding Bigfoot isn't just

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about belief, It's about the journey. Nearly two decades later,

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we're still chasing shadows and sharing stories. The search never stopped.

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Welcome to the Sasquatch Experience.

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Speaker 2: Hello, Get somebody out.

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Speaker 3: Here, and here we go.

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Speaker 4: Welcome to the Sasquatch Experience, everybody, Monday, August eighteenth, twenty

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twenty five. And what a great show we have for

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you all tonight. We've got a full house here as

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you can see down at the bottom. James Baker, vance

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Nesbitt our guest tonight, Terrestrial Henry May and Matt Arner

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from the Allegheny Plateau Project. Great show we're going to have.

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But before we kick off, just a couple of housekeeping notes.

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This weekend, Friday through Sunday is the twenty twenty five

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Pennsylvania Bigfoot Camping Adventure. Myself, Matt and James Baker. We're

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going to be there sponsoring doctor Jeff Meldrum, all representing

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Sasquatch Experience, and we'll be speaking at ten am on Saturday.

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So show up with your selected produce, which, by the way,

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if you're going to throw anything, make sure it's good

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so we can collect it. Or Squatch out Hunger, folks,

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Squatch out Hunger. We still need your help. We've kind

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of stalled in donations. We've got a lofty goal at

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twelve hundred and fifty bucks. We need you, so take

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a minute if you have the time. If you're so inclined,

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go to the website click on the banner to take

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you to Squatch out Hunger. It's a goal, that's it's

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a I don't even want to say it's a goal.

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It's a cause that's really important to us here at

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Sasquatch Experience. We do it every year. Help care about

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our neighbors a little bit and raise some money to

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take care of some folks in need. Every dollar equals

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ten meals through their formulations, so just remember that every

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dollar you donates helping to give ten meals to folks

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in needs. To go ahead and Sasquatch Experience dot com

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click on the banner for Squatch out Hunger. We need

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your help on that and for our patreons, which folks

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you guys, we don't need to remind you, but in

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case you're do, you can join for as little as

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get the fingers up boys two dollars a month. We

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missed our watch along last night because somebody took an

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unplanned early nap, so because of that we'll we spend there,

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of course rescheduling our watch along for another date. So

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you know, as aside from the watch along once again,

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I offer my sincerest apologies.

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Speaker 5: For that.

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Speaker 4: As little as two bucks a month, you get ad

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free shows. Mostly you get episodes of Cryptic Wilderness featuring

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Matt Arner and myself down there at times Baker's Hot Takes,

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which you really don't want to miss. And then the

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Sasquatch Experiences little short form podcasts where I go over

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some recent sightings and share them with you. So that's

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all I got for the news. Anything else from you

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guys before we kick off and I give our intro

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and we start the show thinking off, all right, guys,

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we've all been excited about this one, okay, ever since

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I stumbled upon the Sasquatch Data Project and some of

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the really great, you know, informational slides that she's put

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out there on Instagram. Terrestrial is the creator of the

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Sasquatch Data Project, an ongoing effort to provide researchers with

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Sasquatch report data and a format optimized for data analysis.

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She has a Bachelor of Science and Earth and Atmospheric

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Sciences from the Georgia Institute of Technology, and while in attendance,

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she played an integral role in categorizing, identifying, and measuring

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ground ice features on the Dwarf Planet series as part

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of NASA's don mission. In twenty twenty three, she began

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to development of the Sasquatch Data Project's data set. Her

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work primarily focuses on the application of statistical and NASA

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on reported sasquatch encounters, with particular emphasis of the regional

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variation of behavioral and morphological patterns of reported sasquatches, as

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well as investigating witness psychological responses and summing that up.

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We're just so pleased to have you on the show, Terrestrial.

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Thanks for joining us and putting up with us so

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far for about fifteen minutes.

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Speaker 6: Yeah, thank you so much for having me. I'm like,

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I'm always down to nerd out about data and sasquatch,

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so I'm really excited.

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Speaker 4: Well, you've put together some really great, you know pieces

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of you know, visual representation of that data. I know

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you look, you put out some yesterday the day before

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that were really great where folks can go to Instagram

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and check that out. I guess the first question I

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have is why sasquatch? How did you get involved in

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the sasquatch mystery.

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Speaker 6: Yeah, I've had like kind of a winding path to

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end up creating the Sasquatch Data Project. But really it

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all started when I was about five. I saw Sasquatch

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legend meete science for the first time, and that's where

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I saw the Patterson Gimblin film, and like, I just

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remember seeing that footage in my brain just kind of exploded.

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I was like, this is the craziest thing I've ever seen,

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and I'm also terrified. So but that kind of like

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that kind of I guess planted the seed for me.

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And I had always been really interested and you know,

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read what I could. And then honestly, when Finding Bigfoot

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came out, I was like in middle school and it

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was the first time I realized that people were really

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like actually devoting their time and energy into researching this subject.

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But at the time I didn't really like realize that

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I could do that in an academic setting or in

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like scientific way. So then NASA became goal and that

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was kind of it until I graduated college, and then

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I just landed at doing this. I just realized, like,

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my passion is researching sasquatch, and you know, I love

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data analysis, I love data science. So this is my take,

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I guess on researching this subject.

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Speaker 4: What was really attractive about what you were doing is

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that in twenty twelve we started something up here in

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Pennsylvania called the Keystone Bigfoot Project, and our whole purpose

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was to take all this citing data and put it

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into some sort of like you did, visual representation, and

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so we could go back and look at it historically,

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so see if we could take this data and align

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it to some sort of pattern that we could where

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could we go and best deploy ourselves in the future

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based upon patterns of the past, right, And it was

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quite the mind you menus feet, and none of us

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had the education to do that. I mean, I'm what

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you're doing is rather amazing, and what you've done so

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far is rather amazing. There's a train come through. And

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so that was back in twenty twelve. We started that,

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and we tried to do that. We gave about probably

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about twenty fifteen. We utilized Google Earth as an overlay

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and just kind of, you know, tried to plot it

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that way. But tell us about how you started the

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data project, what was the idea behind it, and how

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did it get going.

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Speaker 6: Yeah, so I guess the whole idea got started because

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I had heard. I've always heard folks say like particular

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things that sasquatches do, or you know, different characteristics that

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they say that they have, but then there were never

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numbers or any kind of you know, substance behind the

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statements that people were making. But the thing that really

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made me start this project was I heard someone say

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that sasquatches are more active under a full moon, and

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I was like, well, you know, as far as I know,

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no one's actually run the numbers on that, like taking

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a large amount of data and actually seeing if that's true.

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So I got started by just with that question in mind,

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and I just started going through the FRORO reports by

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hand and making my spreadsheet. And at that time, you know,

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the data set was pretty small. I probably started off

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with about thirty columns and I've grown it to over

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one hundred and eighty now and I've gone through I

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think like twenty six hundred and fifty nine reports so far,

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so I'm pretty pretty deep. But that was the initial

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question that got me interested and was like, Okay, I've

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got to take this idea that I've been sitting on

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and just do it, Like now's the time.

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Speaker 4: So what have you found out as an answer to

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that question, so far.

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Speaker 6: So far I found out. So something that I like

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to I like to say right off the bat too,

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is when we're taking when we're looking at this data,

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we can make commentary on how reports are basically stated, Like,

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you know, we really can't make statements regarding sasquatch behaviors

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or like their migration habits or anything like that, Like

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we have to look at it from a totally unbiased view.

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So I can't say if they're more active under full

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moon conditions. But I have found that in the data

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that there are more sighting reports in the upper levels

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of the mood illumination, so greater than are equal to

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eighty percent, And that difference, like that increase in citing

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frequency is statistically significant if you're comparing it to the

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middle values. So that's pretty interesting in itself. We also

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see an increase when you are looking at illuminations over

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fifty percent as well. You know why that is, I

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don't know, we can speculate, you know, from both an

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ecological and more humanistic side of things, but it is

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interesting that we do see that, at least so far

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in the data.

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Speaker 4: Right now, the only thing and I'm sure, you would

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probably agree with this is the problem with data up

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to the person reporting it to be as accurate as possible,

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and I at times, you know, given the length of

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time we've investigated it, is that what they remember may

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not always be accurate. So one of the things that

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we've tried to do is, you know, go to resources

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when we've gone out and investigated.

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Speaker 3: Matt and I.

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Speaker 4: Let's say we went out and talked to an investigator

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on Tuesday or a witness on Tuesday, they had a sighting,

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they remember the moon being at one stage, we can

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go and look and say, well, you know, unfortunately you

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might remember it that way. That's just not the case.

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And that's always a problem I had with some of

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this is that folks reporting the data, there's always a

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lot of room for human error.

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Speaker 2: Right.

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Speaker 4: We could do our due diligence and go and correct that,

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but if it's not caught, you know, it could skew it.

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And I think that's that's a problem with data. But

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I mean, have you gone and done any of that

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checking yourself when you're going through and looking at these reports,

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I mean sometimes you're getting very vague statements of those reports.

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Speaker 6: Yeah, I mean I only so when I run any

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kind of data analysis or statistical analysis, I only use

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it when I have complete data, Like I don't. I

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don't really use Class B sightings or class sidings. I'm

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only using Class A sightings really for majority of my analysis.

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And you know, in particular, when we're talking about mud illuminations,

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I'm only using reports that have a complete date so

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that I can go and calculate the muon illumination percentage myself.

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I don't rely on the information in the report. So

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I am very careful with the data that I am using,

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because you know, there is a lot of noise, especially

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when you start introducing Class B reports and things like that.

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But there's also different corrections and stuff that you can

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do when you're running these numbers to make sure that

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the signals that you are seeing are you know, not

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the result of like a false positive or something with

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the statistical testing. So you know, there's ways around it,

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and I try to be as careful as you possibly

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can and only use like the highest quality reports basically.

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But yeah, it really just depends on my question too

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of what I'm wanting to know.

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Speaker 7: I had I had a question, so well, actually it's

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a two part question. One is how many reports did

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you use and how many reports did you disregard because

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they weren't a.

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Speaker 6: So right now for that particular analysis or just in general.

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Speaker 8: Okay, you could pick an analysis or in general.

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Speaker 7: I'm just trying to get to say what the percentage

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between an A and a D is, you know what

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I mean?

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Speaker 4: Like that what you've acquired.

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Speaker 6: So right now on the data set, there are one

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hundred and ninety two Class A reports, There's one thousand,

245
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one hundred and eighty three Class B reports, and then

246
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there's eighteen Class C reports, So it's kind of half

247
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and half right now. And then obviously two like those reports, aren't.

248
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Speaker 2: You know?

249
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Speaker 6: The they don't always have complete data for like everything

250
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that I'm looking for the majority of them don't have

251
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complete data, which is totally fine. But yeah, so that.

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Speaker 7: Makes sense because you didn't ask the questions to get

253
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not to get the answers you wanted, but to get

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the field that you needed to fill in. It's kind

255
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of like if I if me at Forker asked questions

256
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for what we'd like to know, compared you coming in

257
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and asking questions our questions might like Mike might aligne.

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Speaker 8: But some of your.

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Speaker 7: Questions you'd be like, oh, man, I wish they would

260
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have asked it. They were from Tulsa, you know what

261
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I mean? Because it was important to what I needed.

262
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Speaker 8: To know about Tulsa.

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Speaker 2: You know.

264
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Speaker 4: Yeah, No, Beck, you make a great point there, and

265
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I you know, over the years where we've done this,

266
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there are times Treasure where Baker's given me a public

267
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spanking because I'd asked a question that was uh or

268
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I didn't ask enough questions. Wouldn't you agree, Bake? Was

269
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that was at it? I didn't ask enough. I didn't

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go deeper it to go over your.

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Speaker 9: Information with somebody else. That's why partnerships are good, because

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then you can say, okay, hey, I have a eleven

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hundred Class A ones, and of them fifty percent or

274
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seventy five percent of them asked an important question to me. Okay,

275
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and then you have a reference and somebody else is

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gonna go, yeah, but they asked this question that you

277
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missed that reference back to that and you're like, oh, yeah, Ship,

278
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I can reduce paper.

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Speaker 4: Yeah, go ahead.

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Speaker 10: And Matt, So this is actually a follow up to

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James's question for you working in the data field, what

282
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is it that you'd like us as researcher says people

283
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doing the interviews, what sort of information is most important

284
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for your data collection?

285
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Speaker 6: That's a great question, you know. I was actually just

286
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talking to someone the other day too, who is asking

287
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the same thing, like what what uh fields are useful?

288
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Speaker 8: Oh?

289
00:16:42,320 --> 00:16:44,720
Speaker 4: Oh no, you lost her?

290
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Speaker 8: They got rid of her. Tell them to bring her back. Sorry, hello, sorry,

291
00:16:57,320 --> 00:16:57,879
give me up?

292
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Speaker 4: Go ahead.

293
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Speaker 6: So yeah, that's a that's a really great question. When

294
00:17:04,200 --> 00:17:07,279
I am looking at witness reports, honestly, the first thing

295
00:17:07,319 --> 00:17:09,559
I look for is a complete date, so like a

296
00:17:09,720 --> 00:17:12,119
specific date that the encounter happened, because we can derive

297
00:17:12,160 --> 00:17:17,319
a lot of information from that, including like extremely comprehensive

298
00:17:17,359 --> 00:17:21,599
weather data. Obviously we can calculate a moon illumination percentage

299
00:17:22,400 --> 00:17:25,640
and other things as well, like we can potentially use

300
00:17:25,680 --> 00:17:29,559
it for like macent modeling or whatever or not maxcent modeling.

301
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That's lat Loan. A lot of too longitude is my

302
00:17:33,000 --> 00:17:35,799
next thing. My brain was thinking ahead. That's the next

303
00:17:35,799 --> 00:17:37,480
thing that I look for is a lot of two longitude

304
00:17:37,519 --> 00:17:39,200
because we can also drive a lot of information from

305
00:17:39,240 --> 00:17:44,599
that too. But when it comes to like physical descriptions,

306
00:17:44,720 --> 00:17:47,799
that's something that I'm particularly interested in, especially when we're

307
00:17:47,799 --> 00:17:52,160
looking at things on a regional scale. I'm really interested

308
00:17:52,240 --> 00:17:56,759
in like the hair condition, where they maddie or matted

309
00:17:56,960 --> 00:18:01,400
like shaggy nasty, or were they like pretty clean, because

310
00:18:01,839 --> 00:18:05,720
potentially it could tell us about their social dynamics. We

311
00:18:05,759 --> 00:18:08,839
see this and the other great apes things that I

312
00:18:08,880 --> 00:18:13,359
look for are like progmatism, like does their job protrude

313
00:18:13,400 --> 00:18:15,119
from their face or do they have you know, was

314
00:18:15,119 --> 00:18:19,440
it a flat face? You know. I have like a

315
00:18:19,480 --> 00:18:22,119
worksheet I would be happy to send you to that

316
00:18:22,279 --> 00:18:26,279
has basically every column in my data set like spelled

317
00:18:26,279 --> 00:18:29,519
out for it, specifically for people who are doing like

318
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witness follow up interviews that kind of give you an

319
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idea of some things to ask.

320
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Speaker 10: We would we would definitely appreciate that.

321
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Speaker 4: Oh yeah, yeah.

322
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Speaker 6: Yeah, I can definitely send that over. But like I said,

323
00:18:42,799 --> 00:18:45,799
I have like one hundred and eighty columns that I'm

324
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trying to fill out in the spreadsheet for every single report,

325
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So anything related to you know, the environmental, behavioral or

326
00:18:53,359 --> 00:18:57,160
physical traits of sasquatches, like I'm interested in the goal

327
00:18:57,279 --> 00:18:59,680
is to have a column for kind of everything. If

328
00:18:59,720 --> 00:19:02,759
you can ask a question about the report, Like, I

329
00:19:02,799 --> 00:19:06,000
want to have information easily accessible that you can go

330
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and grab from the data set. But yeah, really, really,

331
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the things that are missing that I wish I had

332
00:19:13,480 --> 00:19:15,799
more of is definitely the lat Moan data and then

333
00:19:15,880 --> 00:19:17,640
like a complete date of the encounter, but I know

334
00:19:17,680 --> 00:19:21,680
that's not always possible to get.

335
00:19:25,960 --> 00:19:26,839
Speaker 4: Henry, You're up next.

336
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Speaker 8: You had a question, Yes, I wanted to ask what

337
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sort of patterns do you look for? Do you look

338
00:19:33,319 --> 00:19:36,319
for patterns in the reports that you've collected?

339
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Speaker 6: Yeah, So when it comes to the patterns, I always

340
00:19:42,119 --> 00:19:45,640
have a pre defined question of what I'm asking. I'm

341
00:19:45,680 --> 00:19:51,039
not necessarily looking to answer my questions a certain way.

342
00:19:51,119 --> 00:19:53,880
I just I'll think of something. So like one of

343
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my one of my favorite things that I've looked into

344
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is this relationship between a witness's fear level during the

345
00:20:02,519 --> 00:20:06,319
encounter and the associated estimated height of the sasquatch. So

346
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that was my question because I hear quite often, like

347
00:20:10,079 --> 00:20:12,920
you know, people who are more freaked out, they might

348
00:20:12,960 --> 00:20:15,599
think it's bigger than it actually is, and this is

349
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a very well documented documented psychological phenomenon. So I was like, well,

350
00:20:20,200 --> 00:20:24,359
you know, we could potentially see that in witness reports.

351
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So that's my question. My question is how does or

352
00:20:28,319 --> 00:20:31,960
does the estimated heights of the sasquatch change if you

353
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introduce witness fear levels into things and it turns out

354
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it does. So like I love looking at things like

355
00:20:41,000 --> 00:20:43,400
that that are kind of it doesn't really matter if

356
00:20:43,400 --> 00:20:45,799
the witness is accurate or not in the height estimate,

357
00:20:46,000 --> 00:20:49,000
like in that case, it doesn't only matter if you know,

358
00:20:49,079 --> 00:20:51,519
they said it was eight feet but it was actually

359
00:20:51,519 --> 00:20:53,640
only six and a half feet tall or something. It's

360
00:20:53,680 --> 00:20:57,480
more about just the general change in the data, if

361
00:20:57,480 --> 00:21:00,759
that makes sense. So I love looking for patterns like

362
00:21:00,839 --> 00:21:05,359
that where it would be extremely difficult to hoax, especially

363
00:21:05,400 --> 00:21:12,759
over decades and a large geographic span. And I love

364
00:21:12,839 --> 00:21:14,799
looking at I don't know, just kind of like a

365
00:21:14,839 --> 00:21:19,680
backdoor way into investigating witness reports that might not be

366
00:21:20,519 --> 00:21:24,480
you know, I have a forefront of people's minds.

367
00:21:25,640 --> 00:21:29,920
Speaker 4: Interesting. Oh that's very interesting. I like the purpose behind that.

368
00:21:30,559 --> 00:21:33,119
Terrorized by a four foot tall sasquatch. You know, it

369
00:21:33,240 --> 00:21:37,079
was horrifying. Scare the crap out of me. Hey they're

370
00:21:37,119 --> 00:21:39,839
out there, folks. They start out as baby somewhere right

371
00:21:40,039 --> 00:21:41,920
or the Alba twitch. You got to get a lot

372
00:21:41,920 --> 00:21:45,079
of Alba twitches. Would you have a question in the

373
00:21:45,160 --> 00:21:48,559
chat from Scott Dieter. Lee are a good friend Scott,

374
00:21:48,599 --> 00:21:50,400
and it was to the entire group, but we'll let

375
00:21:50,599 --> 00:21:53,519
Trash will go first. Do you think that the community

376
00:21:53,559 --> 00:21:57,440
could standardize some sort of interview data gathering protocol?

377
00:21:58,559 --> 00:22:03,480
Speaker 6: I think it would be extremely been official because purely

378
00:22:03,599 --> 00:22:09,599
from like a scientific standpoint, having standardization is essential to

379
00:22:09,759 --> 00:22:15,559
any kind of investigation, like making sure you know everyone's asking.

380
00:22:16,559 --> 00:22:19,440
In an ideal world, everyone would ask the same exact

381
00:22:19,519 --> 00:22:22,000
questions in the same exact way, in the same order.

382
00:22:23,200 --> 00:22:28,119
I recognize though that that's not possible really, but we

383
00:22:28,160 --> 00:22:33,000
would definitely definitely benefit from something like that so that

384
00:22:33,079 --> 00:22:35,519
we could make sure that we're extracting as much information

385
00:22:35,599 --> 00:22:39,279
as we possibly can from the witness interviews.

386
00:22:40,680 --> 00:22:44,359
Speaker 7: Yeah, hmm, right.

387
00:22:45,400 --> 00:22:47,440
Speaker 4: I think you have to leave the opportunity for them.

388
00:22:47,720 --> 00:22:51,039
You know, the human aspect of it too. There's you

389
00:22:51,079 --> 00:22:53,839
don't want it to be an interrogation. You want it

390
00:22:53,880 --> 00:22:57,000
to be an interview, right, And sometimes as you're asking

391
00:22:57,599 --> 00:22:59,720
a hell of a lot of questions, that seems more,

392
00:23:00,359 --> 00:23:03,440
you know, of an interrogation then it does an interview.

393
00:23:03,559 --> 00:23:06,359
But if you can answer a lot of the same question.

394
00:23:06,400 --> 00:23:07,839
You know, a lot of the same You can answer

395
00:23:07,839 --> 00:23:10,720
a lot of the questions with a question. It makes

396
00:23:10,759 --> 00:23:13,160
a difference. I think one of the things that it helps, though,

397
00:23:13,319 --> 00:23:16,599
is eliminate leading questions and takes that bias out of

398
00:23:16,640 --> 00:23:21,680
the investigator and puts, like you said, a standard operating

399
00:23:22,799 --> 00:23:25,440
you know, form or data set that you can ask

400
00:23:25,519 --> 00:23:28,680
questions that aren't going to be leading, and you take

401
00:23:28,720 --> 00:23:29,200
a lot.

402
00:23:29,039 --> 00:23:29,359
Speaker 2: Of that.

403
00:23:31,240 --> 00:23:33,000
Speaker 4: I don't want to say guests work. If you take

404
00:23:33,039 --> 00:23:35,160
a lot of that bias out of the equation then

405
00:23:35,880 --> 00:23:42,319
and I think that's really important. Researchers still understand the

406
00:23:42,440 --> 00:23:49,039
power they have in forcing questions out of witnesses or

407
00:23:49,079 --> 00:23:53,039
forcing answers out of witnesses unintentionally, that you're goading them

408
00:23:53,079 --> 00:23:57,279
along and you're you're setting them up. You're not really

409
00:23:57,319 --> 00:24:01,680
helping them give you a true recollection of what they encountered.

410
00:24:04,279 --> 00:24:09,880
It's problematic. I guess my question would be everybody still alive?

411
00:24:10,400 --> 00:24:14,200
Speaker 5: Yeah, we're still alive. I guess my question would be,

412
00:24:15,680 --> 00:24:20,279
thanks man, some of one to ten? Where do you

413
00:24:20,440 --> 00:24:24,960
believe this entity actually exists?

414
00:24:27,599 --> 00:24:27,680
Speaker 4: Like?

415
00:24:28,920 --> 00:24:29,200
Speaker 6: Where?

416
00:24:29,440 --> 00:24:29,680
Speaker 8: Wait?

417
00:24:29,799 --> 00:24:32,160
Speaker 6: Can you say that again? Did you say where they exist?

418
00:24:33,160 --> 00:24:33,440
Speaker 4: Well?

419
00:24:33,640 --> 00:24:37,240
Speaker 5: No, let me. Let me rephrase that on a scale

420
00:24:37,319 --> 00:24:42,599
of one to ten, ten being the greatest, do you

421
00:24:42,720 --> 00:24:46,359
believe that this entity actually exists?

422
00:24:46,720 --> 00:24:50,480
Speaker 6: Oh, actually exists? Sorry, I would say I'm at like

423
00:24:52,119 --> 00:24:57,759
a nine point nine at this point. Awesome, Like yeah,

424
00:24:57,759 --> 00:25:00,440
and doing the data project too is just kind of

425
00:25:00,480 --> 00:25:03,240
pushed me further up the scale as well. Like the

426
00:25:03,240 --> 00:25:07,160
more that I see, the more non random patterns that

427
00:25:07,240 --> 00:25:10,720
I see, the more I'm convinced, especially when you start

428
00:25:10,759 --> 00:25:16,960
recognizing too that some of these patterns mirror known you know,

429
00:25:17,079 --> 00:25:21,559
nocturnal predator behaviors, or they mirror like known great behavior

430
00:25:21,799 --> 00:25:28,599
or you know, anything like that. So yeah, definitely obviously

431
00:25:28,680 --> 00:25:32,559
like seeing one would push me over into a tend.

432
00:25:32,640 --> 00:25:35,160
But the more I look at the data, the more

433
00:25:35,200 --> 00:25:40,799
that I just keep asking questions and keep digging, and

434
00:25:40,920 --> 00:25:41,799
the more I learn it.

435
00:25:41,920 --> 00:25:47,079
Speaker 5: Just I'm just I'm right there, you know, because I

436
00:25:47,119 --> 00:25:49,400
am a lot of people that ask me. They know

437
00:25:49,519 --> 00:25:52,759
that I'm into this field, and they ask me that

438
00:25:52,880 --> 00:25:57,839
same question, and I say, yeah, I definitely believe that

439
00:25:57,880 --> 00:26:02,440
there is this entity that does exist, but I'm not

440
00:26:02,519 --> 00:26:06,799
one hundred percent, So I'm kind of running right alongside

441
00:26:06,799 --> 00:26:13,480
of you with that same analytical belief that, Yeah, I'm

442
00:26:13,480 --> 00:26:17,039
not one hundred percent, but boy, I'm kind of close

443
00:26:17,119 --> 00:26:17,480
to it.

444
00:26:18,880 --> 00:26:21,400
Speaker 4: Well, And I think I'm going to piggyback off you

445
00:26:21,480 --> 00:26:24,359
there for a for a minute advance and go into

446
00:26:24,400 --> 00:26:28,559
another question in terrestrial is you're nine point nine, you know,

447
00:26:28,640 --> 00:26:31,279
a nine point nine on that scale of one to ten,

448
00:26:32,039 --> 00:26:33,880
and you're pretty you know, you did work with NASA.

449
00:26:33,920 --> 00:26:36,920
To me, that's that's pretty scientific. It doesn't get much

450
00:26:36,960 --> 00:26:41,240
more scientific than that. Why do you think science has

451
00:26:41,279 --> 00:26:44,079
a soul has a problem with Bigfoot?

452
00:26:46,359 --> 00:26:51,160
Speaker 6: You know, that's a really good question. I think it's

453
00:26:51,240 --> 00:26:55,839
partly due to the stigma. I mean, obviously there's a

454
00:26:55,839 --> 00:27:00,200
stigma around it, and there's it's kind of wild because

455
00:27:00,200 --> 00:27:02,680
there's even a stigma around just bringing it up as

456
00:27:02,720 --> 00:27:08,960
a question, like within academia. It's definitely people instantly kind

457
00:27:09,039 --> 00:27:12,680
of write you off. For the most part, I think

458
00:27:12,720 --> 00:27:15,400
it's just how it's been handled through the decades.

459
00:27:16,119 --> 00:27:16,319
Speaker 8: You know.

460
00:27:16,400 --> 00:27:18,400
Speaker 6: Personally, I think part of it is due to how

461
00:27:18,440 --> 00:27:22,240
the media handled it and how it kind of how

462
00:27:22,319 --> 00:27:25,960
they kind of shaped society to see the subject as

463
00:27:25,960 --> 00:27:28,720
a whole or to see, yeah, to see the subject

464
00:27:28,759 --> 00:27:32,440
as a whole throughout the decades and whatnot. But I

465
00:27:32,519 --> 00:27:34,480
just think that too. There's a lot of funding on

466
00:27:34,559 --> 00:27:37,599
the line, so people are scared to get their funding

467
00:27:37,640 --> 00:27:41,880
taken away. There. You know, your reputation is very important

468
00:27:42,079 --> 00:27:44,799
in academia and the projects that your on are very important.

469
00:27:44,880 --> 00:27:47,160
So you want people to continue to want to work

470
00:27:47,160 --> 00:27:51,000
with you, and you need again funding. That's a big

471
00:27:51,039 --> 00:27:55,519
part of things. Yeah, I think you know. Obviously I

472
00:27:55,519 --> 00:28:00,720
don't know for sure, but just from my initial thoughts,

473
00:28:01,200 --> 00:28:04,000
I'm assuming that has a lot to do with it.

474
00:28:05,839 --> 00:28:08,759
Speaker 4: That's a good answer. We'll give you points for that one.

475
00:28:08,400 --> 00:28:11,640
Speaker 7: Well, go ahead, I mean you do see that, And

476
00:28:11,880 --> 00:28:16,799
like I'll use Ghostbusters as a common movie that everybody knows.

477
00:28:16,839 --> 00:28:17,759
Speaker 8: But the fact was.

478
00:28:17,759 --> 00:28:21,480
Speaker 7: Is they got thrown out of college because they were

479
00:28:22,039 --> 00:28:25,079
because people in the area noticed what they were doing.

480
00:28:25,799 --> 00:28:28,160
Speaker 8: To a degree, the library incident kind of pushed it

481
00:28:28,200 --> 00:28:30,559
over the edge. But there's other movies.

482
00:28:30,279 --> 00:28:34,759
Speaker 7: Where when someone starts to do paranormal investigation or something

483
00:28:34,799 --> 00:28:38,079
like that, Rose Red's and other movie Stephen King. I

484
00:28:38,079 --> 00:28:40,240
know they're written novel and whatever, but it shows how

485
00:28:40,799 --> 00:28:44,119
them believing in something that the rest of the tenured

486
00:28:44,160 --> 00:28:47,359
academia say, oh no, you can't do that they either

487
00:28:47,400 --> 00:28:50,160
shut up or they think. Yeah, So it is important

488
00:28:50,160 --> 00:28:52,839
to walk the line. And I'm glad that you're able

489
00:28:52,880 --> 00:28:55,000
to do that because that way because the key thing

490
00:28:55,119 --> 00:28:58,559
is is in any of this is research doesn't pay cash.

491
00:28:58,799 --> 00:29:00,640
Speaker 8: You need to have a job, you know.

492
00:29:02,240 --> 00:29:04,839
Speaker 4: Yeah, we'd love to be doing this full time, but

493
00:29:04,920 --> 00:29:09,160
that doesn't exist yet. All right, before we jump into

494
00:29:09,160 --> 00:29:11,759
more questions, we're gonna take our break here, folks. You're

495
00:29:11,799 --> 00:29:14,799
listening to the Sasquatch Experience. Sean Forker, Matt Arnoer, James

496
00:29:14,880 --> 00:29:17,799
Baker Vance and has been our guest Tonight Terrestrial. We'll

497
00:29:17,799 --> 00:29:21,319
be back right after this stay tuned fun show we're

498
00:29:21,319 --> 00:29:23,640
having the night here. We'll be right back with Vance

499
00:29:23,759 --> 00:29:24,920
and the Big Football Worn.

500
00:29:25,799 --> 00:29:28,559
Speaker 3: News team assemboy.

501
00:29:34,759 --> 00:29:37,039
Speaker 5: I think that's why you have so many bigfoot sightings here.

502
00:29:39,119 --> 00:29:44,519
Speaker 4: Good wee people. I think I see bigfoot dude.

503
00:29:44,519 --> 00:29:47,079
Speaker 5: That's a mailbox Because we're downtown. You don't come here.

504
00:29:48,720 --> 00:29:48,960
Speaker 2: Ah.

505
00:29:49,079 --> 00:29:52,839
Speaker 1: Yes, anybody have a big foot costume that can put

506
00:29:52,880 --> 00:29:56,039
over a mailbox, I'll give you five bucks. How about

507
00:29:56,079 --> 00:30:00,880
that We'll show them. Well. Happy even to you all.

508
00:30:01,160 --> 00:30:05,039
I hope you're all surviving the summer swelter and storms.

509
00:30:05,640 --> 00:30:08,000
We certainly had our sheriff storms last night. I think

510
00:30:08,000 --> 00:30:11,119
I'm the only one that still has power on lucky me.

511
00:30:12,359 --> 00:30:15,680
You know, the last time we talked, I mentioned to

512
00:30:15,720 --> 00:30:18,079
you I was doing some research and didn't find any

513
00:30:18,119 --> 00:30:22,839
bigfoot reports for the month of July of this year. Well,

514
00:30:22,880 --> 00:30:26,920
that seems to have changed. And here is a quoted

515
00:30:27,039 --> 00:30:33,039
report to the BFRO by a witness who stated the following.

516
00:30:34,240 --> 00:30:37,519
I was on vacation and just got back yesterday, July

517
00:30:37,720 --> 00:30:43,160
twenty third of twenty twenty five. When this happened. We

518
00:30:43,200 --> 00:30:47,160
rented a cabin outside of Gallenburg, Tennessee. In the hot

519
00:30:47,160 --> 00:30:52,440
tub outside, the woods were silent, then we heard some

520
00:30:52,720 --> 00:30:58,920
tree movement. Me, my wife, and my wife's girlfriend were there. Yes.

521
00:30:59,720 --> 00:31:02,480
Is it wrong for a man and his wife and

522
00:31:02,960 --> 00:31:08,599
girlfriend do share a hot tub? I think not. I

523
00:31:08,720 --> 00:31:11,319
was just looking around the woods and saw what looked

524
00:31:11,359 --> 00:31:15,240
like a black gorilla's head and shoulder stinking out of

525
00:31:15,279 --> 00:31:18,839
the brush about one hundred yards away. The sun was

526
00:31:18,880 --> 00:31:22,079
shining directly on that section of the brush, but it

527
00:31:22,240 --> 00:31:26,319
still looked completely black. I couldn't make out any other

528
00:31:26,400 --> 00:31:30,599
features other than an outline. It did seem like a

529
00:31:30,640 --> 00:31:34,960
fur texture around the edges with a round head. The

530
00:31:35,000 --> 00:31:39,519
blackness of it is what I noticed initially. I stared

531
00:31:39,519 --> 00:31:41,960
at it for a few minutes and thought it looked

532
00:31:42,000 --> 00:31:45,279
like a bigfoot. But I figured I was psyching myself out,

533
00:31:45,440 --> 00:31:49,279
so I intentionally looked away for about a minute, just

534
00:31:49,319 --> 00:31:53,359
staring at the cabin wall. When I looked back, it

535
00:31:53,480 --> 00:31:58,079
was gone. Another minute or two later, we heard trees crackling.

536
00:31:58,880 --> 00:32:01,680
I looked around for about another ten minutes and didn't

537
00:32:01,720 --> 00:32:06,440
see anything else, no other experiences. Why we were there.

538
00:32:07,519 --> 00:32:09,799
Maybe it was a mailbox in the bush that fell over.

539
00:32:10,839 --> 00:32:15,880
Who knows when we come back. Yes, we're gonna promote

540
00:32:15,880 --> 00:32:19,759
it for the last time. Let's go camping.

541
00:32:22,880 --> 00:32:26,519
Speaker 6: You're listening to the big footballhorn right here on the Sasquatch.

542
00:32:26,000 --> 00:32:41,400
Speaker 1: Experience as anybody ever told you. Hey, nice knockers. If not,

543
00:32:41,920 --> 00:32:46,359
just visit got knockers dot org read about the encounter

544
00:32:46,480 --> 00:32:50,720
that kicked this whole thing off. Plus you comparchase one, two,

545
00:32:50,880 --> 00:32:54,599
three or more Tree Knockers. I'm not sure what you

546
00:32:54,720 --> 00:32:58,400
thought we were talking about. Got Knockers has a plethora

547
00:32:58,480 --> 00:33:02,599
of gifts and merchandise too, from awesome apparel, ware, and

548
00:33:02,880 --> 00:33:07,880
even something for the baby. Find jewelry and sausage to

549
00:33:07,960 --> 00:33:11,519
please anyone's taste. Bud stop on buy and give a

550
00:33:11,519 --> 00:33:15,200
hello to Gwendolyn and Michael Perse. Now that you know

551
00:33:15,240 --> 00:33:18,799
where to gets some great knockers. They make fun gifts

552
00:33:18,920 --> 00:33:24,039
or accessories for the squatch bag. Just visit Godknockers dot

553
00:33:24,160 --> 00:33:49,000
org again got knockers dot org. Well, folks were only

554
00:33:49,200 --> 00:33:53,440
four and a half days away from the PA Bigfoot

555
00:33:53,559 --> 00:33:58,559
camping Adventure. It's this Friday and Saturday, August twenty second

556
00:33:58,720 --> 00:34:07,640
and twenty at the Benners Metal Run RV Campground in Farmington, Pennsylvania.

557
00:34:08,400 --> 00:34:12,239
An exciting lineup of speakers out of the sixteen, here's

558
00:34:12,400 --> 00:34:18,239
just naming a few. Ron Moorhead, Sean Forker, Lyle Blackburn,

559
00:34:18,840 --> 00:34:24,559
Stan Gordon Matt Arner, and of course doctor Jeff Meldrum.

560
00:34:25,000 --> 00:34:28,280
They'll be workshops so you can dive into Bigfoot sightings,

561
00:34:28,400 --> 00:34:32,599
evidence and analysis and the science behind the legend. They'll

562
00:34:32,639 --> 00:34:36,440
also be night hikes join a guided search deep into

563
00:34:36,480 --> 00:34:39,880
the woods. Who knows what you might find. They'll be

564
00:34:39,960 --> 00:34:44,119
live music in movies to enjoy entertainment all weekend long.

565
00:34:44,239 --> 00:34:47,800
Plus they'll be vendors and food trucks so that you

566
00:34:47,880 --> 00:34:52,960
can grab some delicious food and shop for unique Bigfoot gear.

567
00:34:54,159 --> 00:35:01,039
Just go to PA Bigfoot Campingadventure dot com. Again PA

568
00:35:01,400 --> 00:35:07,400
Bigfoot Camping Adventure dot Com, thanks again for listening to

569
00:35:07,440 --> 00:35:10,840
this edition of the big Footbullhorn right here on Anomalous

570
00:35:11,000 --> 00:35:17,920
Entertainment's Sasquatch Experience. And of course, as always, until we

571
00:35:18,039 --> 00:35:20,199
meet again, keep your toe in the mind.

572
00:35:20,280 --> 00:35:35,760
Speaker 4: Mind Now, that was really good dvance, well done, well done.

573
00:35:35,840 --> 00:35:41,480
My friend father, yes, and you know he comes with

574
00:35:41,480 --> 00:35:43,119
a mute button, folks, but he doesn't know how to

575
00:35:43,159 --> 00:35:45,000
use it. The bull in the China Shop, James Baker.

576
00:35:45,039 --> 00:35:49,760
Everybody back in the chair, lacking WD forty. But before

577
00:35:49,760 --> 00:35:51,079
we went to the break, we were asking us some

578
00:35:51,079 --> 00:35:54,079
really good questions of our guest Tonight Terrestrial. If you're

579
00:35:54,119 --> 00:35:56,679
just joining us at the midpoint of the show, for

580
00:35:56,760 --> 00:35:59,440
the love of god, hit your mute button, Baker, and uh,

581
00:36:00,199 --> 00:36:03,960
we have I asked for one hour a week. That's

582
00:36:04,360 --> 00:36:08,440
one hour a week. Anyhow, we have trestoral on the

583
00:36:08,440 --> 00:36:10,920
show from the Sasquatch Data Project. We had a great

584
00:36:11,280 --> 00:36:13,440
you know discussion at the beginning, and we're going to

585
00:36:13,480 --> 00:36:17,199
carry on with that. Henry has looks like he left

586
00:36:17,440 --> 00:36:21,360
or hopefully he's okay, Matt Baker, vance any questions before

587
00:36:21,400 --> 00:36:24,320
I move on to the next segment. Yeah, you know what,

588
00:36:25,679 --> 00:36:31,840
Oh go ahead, No, I'm going into that question. You do, Okay.

589
00:36:32,440 --> 00:36:36,079
Speaker 10: So, from what I understand, Thrustol is you're taking data

590
00:36:36,159 --> 00:36:40,079
from the the BFRO sidings. Correct, they're the Class a's.

591
00:36:40,639 --> 00:36:47,119
Have you looked in perhaps started looking at possibly in

592
00:36:47,199 --> 00:36:52,559
putting the information from historical reports like uh, those that

593
00:36:52,760 --> 00:36:57,239
John Green had had written in his book, or even

594
00:36:57,280 --> 00:37:00,840
going back further with some of the some of the

595
00:37:02,159 --> 00:37:05,159
reports before even you know, Bigfoot was in me.

596
00:37:06,159 --> 00:37:09,800
Speaker 6: Yeah, so I actually I actually just got access to

597
00:37:09,920 --> 00:37:13,199
John Green's data SEID the other day. So I'm starting

598
00:37:13,239 --> 00:37:16,280
to look through that and see what I can do

599
00:37:16,320 --> 00:37:18,719
with it and see how I can incorporate it into

600
00:37:18,760 --> 00:37:22,119
the data set that I'm making. The eventual goal of

601
00:37:22,159 --> 00:37:26,480
this is to basically have a massive catalog of reports.

602
00:37:26,599 --> 00:37:30,559
So the bf O is just step one, and you know,

603
00:37:30,719 --> 00:37:33,320
it is just me working on this, so it takes

604
00:37:33,400 --> 00:37:37,599
a while. You know, I'm not manually I'm manually reviewing reports,

605
00:37:37,639 --> 00:37:42,079
but I don't manually input them anymore. I'm using AI

606
00:37:42,119 --> 00:37:45,000
to help me do that. Essentially created this like whole

607
00:37:45,000 --> 00:37:49,320
pipeline that does it for me. But yeah, the the

608
00:37:49,320 --> 00:37:53,480
the eventual goal is to incorporate pretty much as as

609
00:37:53,480 --> 00:37:57,360
many reports as possible, historical, you know, all the way

610
00:37:57,440 --> 00:38:02,000
up to present day. So yeah, I'm starting to go

611
00:38:02,079 --> 00:38:05,519
through John Green's data set and see what that's all about,

612
00:38:05,559 --> 00:38:08,480
which I'm really excited about. And I do have a

613
00:38:08,519 --> 00:38:12,000
few reports from like the North America Wedding Conservancy as

614
00:38:12,039 --> 00:38:15,920
well in the data set. But yes, yeah, let's see

615
00:38:16,000 --> 00:38:16,760
eventual goal.

616
00:38:17,719 --> 00:38:25,159
Speaker 4: Awesome, awesome, Yeah Baker or Vance? What at a time?

617
00:38:27,760 --> 00:38:30,480
Speaker 8: Van Vance is dead? Where did he go?

618
00:38:33,039 --> 00:38:37,000
Speaker 7: Okay, sorry I was a little late to the chair

619
00:38:37,719 --> 00:38:41,760
at some extra problems to deal with. Anyway, First I

620
00:38:41,800 --> 00:38:44,440
want to thank you for answering our questions. And secondly

621
00:38:44,480 --> 00:38:46,599
it is I want to think thank you for trying

622
00:38:46,599 --> 00:38:48,679
to put this data out there, because I think a

623
00:38:48,719 --> 00:38:52,320
lot of times we get do you.

624
00:38:51,960 --> 00:38:54,880
Speaker 8: Do you have trouble producing your data?

625
00:38:55,719 --> 00:38:58,039
Speaker 7: I'm gonna put it this way, getting your data out

626
00:38:58,119 --> 00:39:01,440
because of the latest fake site and the latest problems

627
00:39:01,480 --> 00:39:03,599
we've had that most news that comes out of the

628
00:39:03,599 --> 00:39:07,800
Bigfoot community as fifty percent is bunkable, if we'll call that.

629
00:39:08,880 --> 00:39:15,440
Speaker 6: Yeah, no, I you know, I know just because I'm using, uh,

630
00:39:15,679 --> 00:39:20,079
what the BFO has available. I honestly, I gotta admit,

631
00:39:20,159 --> 00:39:23,159
I don't really keep up with what's you know, the

632
00:39:23,280 --> 00:39:28,320
latest sighting or video is because you know, the majority

633
00:39:28,400 --> 00:39:31,280
of it is just, at least on the video standpoint,

634
00:39:31,280 --> 00:39:33,239
the majority of it it just hoaxes.

635
00:39:34,679 --> 00:39:36,960
Speaker 8: And so I do that's the best answer, because that's

636
00:39:37,000 --> 00:39:39,079
how I feel. I don't look at any of it.

637
00:39:39,519 --> 00:39:42,280
Speaker 6: Yeah, I honestly don't really keep up with it. My

638
00:39:42,480 --> 00:39:47,239
brain is purely on getting through these reports and looking

639
00:39:47,280 --> 00:39:50,599
through the data set, so I don't really let it

640
00:39:50,679 --> 00:39:54,960
influence what I'm doing, honestly, and for me getting this

641
00:39:55,079 --> 00:39:57,599
information out there is I'm just I'm just trying to

642
00:39:57,719 --> 00:40:01,559
present the numbers and then you know, kind of go

643
00:40:01,679 --> 00:40:05,639
from there. I really try to not take any kind

644
00:40:05,679 --> 00:40:09,199
of strong stance on anything. I try to remain very

645
00:40:09,239 --> 00:40:13,280
neutral as unbiased as possible and just kind of say, well,

646
00:40:13,360 --> 00:40:15,719
this is what the data is saying, and you know,

647
00:40:16,039 --> 00:40:18,159
you could take this this way or this way, or

648
00:40:18,199 --> 00:40:20,800
it's something else that we don't know about, but it's

649
00:40:20,840 --> 00:40:24,079
interesting that this is the pattern that we're seeing. Basically,

650
00:40:25,559 --> 00:40:27,480
so I kind of I try to let the numbers

651
00:40:27,480 --> 00:40:31,559
speak for themselves, and you know, people are free to

652
00:40:31,639 --> 00:40:35,559
interpret the results as they will, but I just think

653
00:40:35,559 --> 00:40:37,760
it's really important to do that. I think it's really

654
00:40:37,760 --> 00:40:42,480
important to not really a strong stance. So yeah, I

655
00:40:42,519 --> 00:40:43,599
hope I answered your question.

656
00:40:43,880 --> 00:40:47,440
Speaker 7: No, no, you you you answered. You actually answered a

657
00:40:47,440 --> 00:40:50,119
better question than I asked. Because the fact is.

658
00:40:50,320 --> 00:40:53,119
Speaker 8: That that I think that your your stance on it.

659
00:40:53,159 --> 00:40:55,239
Speaker 7: And the nice thing is is when somebody confronts you

660
00:40:55,280 --> 00:40:57,360
and says, hey, you're just flulllah blah blah blah, You'll

661
00:40:57,400 --> 00:40:59,920
be like, see this big pack of papers, this is

662
00:41:00,119 --> 00:41:03,199
what I started with, this is what I've got and

663
00:41:03,280 --> 00:41:05,599
since you didn't read either one of these, you can

664
00:41:05,679 --> 00:41:06,760
go away.

665
00:41:07,119 --> 00:41:09,199
Speaker 8: That's the positive way to say it.

666
00:41:09,199 --> 00:41:12,440
Speaker 9: I would have maybe added a little extra Urban Dictionary, but.

667
00:41:12,440 --> 00:41:18,159
Speaker 4: Yeah, yeah, you put a lot of truth in there, Baker.

668
00:41:18,360 --> 00:41:21,960
You know, with what you're presenting terrestrial, right, it's unemotional,

669
00:41:22,639 --> 00:41:27,119
it's it's fact from the information you have, right, and

670
00:41:27,400 --> 00:41:29,280
and people have a hard time with that. I'm even

671
00:41:29,360 --> 00:41:31,480
listen going through and looking at some of the comments

672
00:41:31,679 --> 00:41:34,159
you know in the chat room right now, like you know,

673
00:41:34,239 --> 00:41:37,599
someone miffed off about you know, plenty of real video

674
00:41:37,760 --> 00:41:40,960
for your information. Oh, there's not. I have yet to

675
00:41:41,000 --> 00:41:43,800
see something that's compelling enough to say, oh hot, damn,

676
00:41:43,880 --> 00:41:46,840
that's a big foot. Just doesn't It hasn't happened yet.

677
00:41:47,360 --> 00:41:49,599
Speaker 8: But squads of videos are pretty good though.

678
00:41:49,719 --> 00:41:51,280
Speaker 4: Yeah, well, you know, we have a lot of fun

679
00:41:51,320 --> 00:41:54,599
with those AI videos as well. But the reality is

680
00:41:54,679 --> 00:41:58,960
all that stuff's clogging up the pipe. It's the pipe.

681
00:41:59,159 --> 00:42:00,840
And when you can look at it and break it

682
00:42:00,920 --> 00:42:03,480
down and you're just looking at raw data, you know,

683
00:42:03,559 --> 00:42:06,800
this is what it shows. That's a that's a great

684
00:42:06,800 --> 00:42:10,920
starting point or a great just factual point, like you

685
00:42:11,039 --> 00:42:14,800
might feel that way, but this data is a bias,

686
00:42:14,920 --> 00:42:17,760
you know, this is this is telling a different story

687
00:42:17,480 --> 00:42:20,400
than in how you feel, and it's important we get

688
00:42:20,440 --> 00:42:22,599
into that mindset. We're going to keep doing the same

689
00:42:22,679 --> 00:42:24,679
thing that we've been doing for the last sixty years,

690
00:42:24,920 --> 00:42:30,519
which is chasing our perpetual tales and not speaking for

691
00:42:30,559 --> 00:42:34,000
everybody else here. You know, I've had an experience with

692
00:42:34,079 --> 00:42:38,360
something I can't explain that it was really impactful to me.

693
00:42:38,559 --> 00:42:41,639
It was very I don't want to say life changing

694
00:42:41,679 --> 00:42:42,840
because that's super.

695
00:42:43,960 --> 00:42:44,519
Speaker 6: You know, like.

696
00:42:46,039 --> 00:42:51,679
Speaker 4: Dramatic, but it it changed my thought process onto the

697
00:42:51,760 --> 00:42:54,519
subject that brought it into reality for me, but I'm

698
00:42:54,559 --> 00:42:57,880
still very skeptical. I think having an experience of my

699
00:42:57,920 --> 00:43:01,199
own made it worse and van you know it was,

700
00:43:01,400 --> 00:43:04,760
you know really, And I'm bringing back advance back on

701
00:43:04,880 --> 00:43:08,159
vance oaticiting with something he couldn't explain years ago. And

702
00:43:08,239 --> 00:43:11,679
I think that when you have an encounter or you

703
00:43:11,719 --> 00:43:14,079
have an experience with something and there's no definitive to

704
00:43:14,119 --> 00:43:17,760
what you've seen. To me, that's made me a little

705
00:43:17,800 --> 00:43:20,960
bit more hostile to these folks that are adamant, like

706
00:43:21,880 --> 00:43:24,679
I what gives you the right to say that we

707
00:43:24,719 --> 00:43:29,119
don't have anything to base this off of, Like, as

708
00:43:29,239 --> 00:43:33,199
much as we want to say this is real, there's

709
00:43:33,239 --> 00:43:41,519
still no proof positive that it is. Yeah, at least

710
00:43:41,559 --> 00:43:45,119
with the data and the tremendous amounts of it. It

711
00:43:45,159 --> 00:43:48,639
gives us something to lean on and say, well, we

712
00:43:48,719 --> 00:43:51,840
might not have a body, but we do have this,

713
00:43:52,480 --> 00:43:54,599
and thank you for trying to help put that into

714
00:43:54,599 --> 00:43:57,239
a way that's understandable for everybody.

715
00:43:57,840 --> 00:44:00,719
Speaker 8: But anything you make sorry, I didn't mean oh.

716
00:44:00,599 --> 00:44:03,000
Speaker 6: No, sorry, I was just gonna say, like, yeah, that's

717
00:44:03,079 --> 00:44:05,880
kind of the basis of what I guess what I'm

718
00:44:05,920 --> 00:44:08,320
trying to get at is, like we we need to

719
00:44:08,360 --> 00:44:13,079
start using numbers and information and like statistical analysis or

720
00:44:13,159 --> 00:44:16,639
data analysis or whatever as the basis of our ideas

721
00:44:17,679 --> 00:44:20,880
towards the subject instead of the other way around, right like,

722
00:44:21,039 --> 00:44:25,719
just like we need to have something more substantial to

723
00:44:25,760 --> 00:44:28,760
start forming our ideas. And that's what I'm really hoping

724
00:44:28,920 --> 00:44:31,960
to do, Like, I'm really hoping that I can present

725
00:44:32,000 --> 00:44:35,079
this information and that you know, the data set is available,

726
00:44:35,119 --> 00:44:39,320
so you know, people can go through it, you know,

727
00:44:39,440 --> 00:44:43,360
and see what they can find out. But that's you know,

728
00:44:43,440 --> 00:44:46,480
a pretty big reason why I started this was because

729
00:44:46,760 --> 00:44:48,800
I was just I was hearing a lot of ideas

730
00:44:48,840 --> 00:44:51,840
and thoughts and things, but I wasn't seeing a lot

731
00:44:51,840 --> 00:44:56,320
of information to back up statements. So yeah, at least

732
00:44:56,360 --> 00:45:00,920
with at least with the data. You know, it's something, right, it's.

733
00:45:00,760 --> 00:45:04,400
Speaker 7: Yeah, something I wouldn't cheapen what you're doing. And you

734
00:45:04,440 --> 00:45:07,559
did make one valid point, is you saw what you

735
00:45:07,679 --> 00:45:12,119
wanted to know and instead of complaining or crying or

736
00:45:12,199 --> 00:45:16,360
making shit up, sorry, you took the raw data and said, Okay,

737
00:45:16,400 --> 00:45:20,320
does this match my hypothesis? And that's that's the best

738
00:45:20,400 --> 00:45:23,199
kind of science. That's like Sean said earlier, is you know,

739
00:45:23,480 --> 00:45:25,920
I've seen the difference from when he had a sighting

740
00:45:25,960 --> 00:45:28,360
to when he didn't have a sighting, on how he

741
00:45:29,280 --> 00:45:32,519
handles witnesses, how he handles facts, how he handles a

742
00:45:32,519 --> 00:45:36,119
lot of things. I also, I've changed my opinion on

743
00:45:36,639 --> 00:45:39,519
how I feel about whether it's a creature. My opinion

744
00:45:39,559 --> 00:45:43,360
has never varied from it's either completely alive or we've

745
00:45:43,360 --> 00:45:45,280
been wasting seventy five years.

746
00:45:45,719 --> 00:45:47,039
Speaker 8: Do you know what I mean, It's one or the other.

747
00:45:47,320 --> 00:45:50,639
Speaker 7: But when Sean had a sighting, to listen to him

748
00:45:50,679 --> 00:45:52,679
and to see what he did, and to see how

749
00:45:52,719 --> 00:45:56,360
he's changed and how he does things, I believe more

750
00:45:56,599 --> 00:46:00,000
in the possibility of a creature than I did before

751
00:46:00,079 --> 00:46:03,800
for that because I trust in him, and I trust

752
00:46:03,840 --> 00:46:06,719
in what you're saying, because you've got a book that

753
00:46:06,840 --> 00:46:08,719
has the data if I want to look it up

754
00:46:08,760 --> 00:46:11,360
and read it, which you can ask for. Or I

755
00:46:11,360 --> 00:46:13,400
get mad at all kinds of things from the internet,

756
00:46:13,480 --> 00:46:16,159
like they say, blah blah blah this the this guy said,

757
00:46:16,159 --> 00:46:16,800
blah blah blah.

758
00:46:16,840 --> 00:46:19,719
Speaker 8: Okay, where's the quote? Oh, I don't have any shut up?

759
00:46:20,039 --> 00:46:22,519
Speaker 6: Okay, we done.

760
00:46:22,760 --> 00:46:25,280
Speaker 8: But I just wanted I wanted to thank you for

761
00:46:25,400 --> 00:46:28,800
seeing a problem that didn't fit what you needed and

762
00:46:28,840 --> 00:46:31,760
then looking and making sure that the evidence fit it,

763
00:46:31,840 --> 00:46:33,920
not that you fit it to the evidence.

764
00:46:36,519 --> 00:46:39,960
Speaker 6: Yeah, I'm happy, happy to very happy to do that

765
00:46:40,880 --> 00:46:43,280
because I just like I respect for that.

766
00:46:43,360 --> 00:46:46,840
Speaker 7: I'm not you know, that's I wish I saw more

767
00:46:46,840 --> 00:46:47,800
of it in the community.

768
00:46:49,079 --> 00:46:53,159
Speaker 6: I really appreciate that because it's definitely definitely needed. We

769
00:46:53,199 --> 00:46:56,840
need more of this, and you know, I just I

770
00:46:56,880 --> 00:46:59,320
hope to help in any way that I can, like

771
00:46:59,599 --> 00:47:03,960
just make more accessible or make it easier to interpret

772
00:47:04,119 --> 00:47:06,840
or whatever. Like, I'm always happy to answer data questions

773
00:47:06,880 --> 00:47:08,639
and stuff too, if people ever have them, Like, I'm

774
00:47:08,639 --> 00:47:10,679
always happy to go dig through the data sent and

775
00:47:10,719 --> 00:47:15,159
see what comes up. But yeah, I really really hope

776
00:47:15,199 --> 00:47:18,320
that that's what I'm put out there at least, is

777
00:47:18,360 --> 00:47:19,280
that kind of thinking.

778
00:47:19,760 --> 00:47:23,920
Speaker 10: Yeah, well, you know what I have. I have a

779
00:47:24,000 --> 00:47:26,440
question here, and I'm going to kind of take this

780
00:47:27,639 --> 00:47:30,800
conversation or a whole new area here. We've been talking

781
00:47:30,840 --> 00:47:33,519
a lot about data. We've been talking a lot about

782
00:47:34,159 --> 00:47:38,960
you know, going through it and you know, analyzing it.

783
00:47:39,960 --> 00:47:43,599
Let's talk about something completely different. Have you had any

784
00:47:43,800 --> 00:47:47,400
experiences and have you done any field research. Have you

785
00:47:47,480 --> 00:47:49,480
been out in the in the field and tell us

786
00:47:49,480 --> 00:47:49,920
about it.

787
00:47:50,679 --> 00:47:55,280
Speaker 6: Yeah, so I would say, I the field work is

788
00:47:55,280 --> 00:47:57,159
something that I really want to get into. I just

789
00:47:57,199 --> 00:48:01,960
haven't had really an opportunity. I will say though, that

790
00:48:02,039 --> 00:48:06,760
my my parents own a farm in Georgia, and every

791
00:48:06,760 --> 00:48:09,119
time we go back to visit, I'm basically in the woods,

792
00:48:09,159 --> 00:48:11,679
like twenty four to seven. Because I have had some

793
00:48:12,679 --> 00:48:16,400
weird things happen there. I can't definitively say that it

794
00:48:16,519 --> 00:48:20,880
was you know, the experiences were due to sasquatches, but

795
00:48:21,000 --> 00:48:25,039
also I have no idea, you know, what it was.

796
00:48:25,760 --> 00:48:29,880
Everything was auditory. My dad did see a big, a

797
00:48:29,880 --> 00:48:31,880
big dude though, running out of our barn once, so

798
00:48:31,920 --> 00:48:36,039
that was interesting. But you know, he can't he can't

799
00:48:36,199 --> 00:48:39,119
say for sure that it was a sasquatch. But you know,

800
00:48:39,159 --> 00:48:44,119
we've had We've had some weird stuff. Probably the weirdest

801
00:48:44,119 --> 00:48:49,119
thing that I've had happen is I woke up one

802
00:48:49,199 --> 00:48:53,480
night and I heard what sounded. The only way I

803
00:48:53,480 --> 00:48:56,199
can describe it is like it was a super demented turkey,

804
00:48:56,639 --> 00:48:59,000
Like I know, that's so weird, but it sounded like

805
00:48:59,039 --> 00:49:01,920
a really demented turkey gobble with like a horse snort

806
00:49:02,000 --> 00:49:06,440
at the end of it. It was so bizarre. And

807
00:49:06,920 --> 00:49:11,920
along with this, I heard very distinctive bipedal running in

808
00:49:11,960 --> 00:49:16,440
the woods, just back and forth in the woodline, and

809
00:49:16,480 --> 00:49:20,840
I'm hearing this through my wall and I'm like, I

810
00:49:20,840 --> 00:49:22,519
don't know what this is. I'm looking out my window.

811
00:49:22,599 --> 00:49:25,400
I can't see anything. My dogs are totally silent, which

812
00:49:25,440 --> 00:49:29,440
was weird. And it was just a very odd experience.

813
00:49:29,480 --> 00:49:32,280
And that went on for roughly like five to seven

814
00:49:32,320 --> 00:49:34,760
minutes and then it just stopped. Never heard it again.

815
00:49:35,480 --> 00:49:39,519
Oh wow, So it's just like very weird like auditory stuff.

816
00:49:39,559 --> 00:49:42,519
But again I can't I cannot at all say that

817
00:49:42,519 --> 00:49:47,920
that was the product of a sasquatch. But yeah, so.

818
00:49:48,440 --> 00:49:51,239
Speaker 1: Yeah, I was trying to get out to the I

819
00:49:51,280 --> 00:49:53,239
would ask one question, how many.

820
00:49:53,079 --> 00:49:57,400
Speaker 5: Dogs were there involved two?

821
00:49:58,119 --> 00:50:01,960
Speaker 6: I had two outside dogs that barked at everything. And

822
00:50:02,039 --> 00:50:05,840
I remember sitting there thinking, this is so weird that

823
00:50:05,880 --> 00:50:09,320
they're not barking because they always bark. And then it

824
00:50:09,360 --> 00:50:12,400
turns out that that's, you know, that's something I keep

825
00:50:12,480 --> 00:50:16,039
up with in the data set. That's pretty common. Well,

826
00:50:16,079 --> 00:50:19,000
I hesitate to say common. It shows up more than

827
00:50:19,039 --> 00:50:23,800
you would think, especially in the Class A sightings. So yeah,

828
00:50:24,800 --> 00:50:25,119
I think.

829
00:50:25,400 --> 00:50:30,079
Speaker 10: We've had discussions in the past about how animals react

830
00:50:31,119 --> 00:50:33,960
to different sightings, and I think that that's something that's

831
00:50:34,079 --> 00:50:37,440
interesting to put in the data as well. You get

832
00:50:37,440 --> 00:50:41,400
some dogs at that'll hel and you know, go running

833
00:50:41,440 --> 00:50:44,800
off and try to attack whatever's out there. You get

834
00:50:44,800 --> 00:50:49,840
other ones that big mean, you know, dogs that come

835
00:50:50,639 --> 00:50:52,880
running back with their tail between their legs, And I

836
00:50:53,440 --> 00:50:57,920
do think that that's possibly an important piece of data

837
00:50:58,000 --> 00:50:59,440
that could be included as well.

838
00:51:00,480 --> 00:51:02,599
Speaker 6: Yeah, it's definitely something that I'm keeping up with. I

839
00:51:02,639 --> 00:51:05,559
have calls for it in the data set, you know,

840
00:51:05,599 --> 00:51:08,239
I just haven't run numbers on it. I should do

841
00:51:08,280 --> 00:51:12,559
that though, I probably have enough a large enough sample

842
00:51:12,599 --> 00:51:16,280
size at this point to look into that. Yeah, I

843
00:51:16,280 --> 00:51:18,119
can do that. I can run some numbers.

844
00:51:18,440 --> 00:51:20,920
Speaker 10: So so one last question about the field research. With

845
00:51:21,039 --> 00:51:25,360
everything that you know from the data, when would you

846
00:51:25,480 --> 00:51:29,599
think the best time of year to be out in

847
00:51:29,639 --> 00:51:31,280
the field doing research would be?

848
00:51:32,039 --> 00:51:37,199
Speaker 6: Yeah, So that question gets tricky because when we're looking

849
00:51:37,199 --> 00:51:40,679
at these patterns in the data, it's difficult to say

850
00:51:40,840 --> 00:51:45,519
if the patterns are due to you know, ecological reasons

851
00:51:45,880 --> 00:51:50,000
or a biological reason, or if it's due to you know,

852
00:51:50,159 --> 00:51:53,079
human patterns. So in the data set, there is an

853
00:51:53,119 --> 00:51:58,400
overwhelming majority of summer and fall class A sightings. And

854
00:51:58,840 --> 00:52:01,159
you know, as we know, this up really well with

855
00:52:01,480 --> 00:52:05,239
when humans are getting out you know outside, they're camping,

856
00:52:05,280 --> 00:52:09,880
they're hiking more, and they're hunting, especially in the fall. Actually,

857
00:52:09,920 --> 00:52:12,599
just read a paper the other day that like this

858
00:52:12,760 --> 00:52:14,880
you would think be intuitive, but you know, there's got

859
00:52:14,920 --> 00:52:17,519
to be a paper on it. You know, humans are

860
00:52:17,519 --> 00:52:20,400
more likely to be in remote places, they go further

861
00:52:20,519 --> 00:52:23,360
out into remote places during the fall versus any other

862
00:52:23,400 --> 00:52:26,480
time of the year, you know, because they're hunting and

863
00:52:26,480 --> 00:52:31,440
they're camping and whatever. So it's hard to say when

864
00:52:31,480 --> 00:52:34,280
the best time is. I will say there's a pretty

865
00:52:35,039 --> 00:52:38,519
large lack of sightings in the springtime, which is odd

866
00:52:38,559 --> 00:52:41,599
when you're looking at things from like a an animal

867
00:52:42,199 --> 00:52:45,320
kind of perspective, because usually they're you know, recovering from

868
00:52:45,320 --> 00:52:48,360
winter and they're foraging and they're out and about. So

869
00:52:48,400 --> 00:52:51,199
that's kind of interesting in itself and almost more interesting

870
00:52:51,239 --> 00:52:54,719
to me than the big spike that we have in

871
00:52:54,760 --> 00:52:58,920
the summer fall. So yeah, it gets it gets hard.

872
00:52:59,039 --> 00:53:01,239
It gets hard to say like when the best time

873
00:53:01,280 --> 00:53:03,840
to go is, because we don't know if these patterns

874
00:53:03,880 --> 00:53:07,519
are due to you know the animals themselves or due

875
00:53:07,559 --> 00:53:08,480
to people.

876
00:53:12,079 --> 00:53:13,960
Speaker 10: And Sean.

877
00:53:15,679 --> 00:53:18,840
Speaker 4: We do have a couple of questions from Scott on

878
00:53:19,079 --> 00:53:23,400
the from our patreon. Scott asks, what are key data

879
00:53:23,440 --> 00:53:27,719
points that researchers and investigators should gather related to settings

880
00:53:27,760 --> 00:53:30,840
or encounters that would help build better databases of information.

881
00:53:31,599 --> 00:53:32,440
That's one question.

882
00:53:33,079 --> 00:53:37,280
Speaker 6: So definitely if if you can get a complete date

883
00:53:37,480 --> 00:53:41,360
for the encounter, that's super helpful. We can derive a

884
00:53:41,400 --> 00:53:46,320
lot of information from that, and particularly like environmental information

885
00:53:47,039 --> 00:53:51,679
lat moms are super helpful, and I think too if

886
00:53:51,719 --> 00:53:56,719
we can consistently gather data as well on like the

887
00:53:56,760 --> 00:54:01,320
witnesses occupation, how comfortable they are in the woods as well.

888
00:54:01,920 --> 00:54:04,880
Those are two things that I've been looking into recently

889
00:54:05,599 --> 00:54:07,800
that' kind of piqued my interest to see if there's,

890
00:54:07,840 --> 00:54:11,159
you know, are there any inherent differences between the sightings

891
00:54:11,440 --> 00:54:15,280
from people who are highly experienced in the woods versus

892
00:54:15,320 --> 00:54:20,280
those who are not things like that. So I would

893
00:54:20,320 --> 00:54:25,559
say the probably those four things, And it really depends

894
00:54:25,559 --> 00:54:28,880
on your research question too, Like it depends on what

895
00:54:29,000 --> 00:54:33,719
you're wanting to know, because like the STATA set's never ending,

896
00:54:34,559 --> 00:54:36,679
and I'm like I'm trying to keep up with so

897
00:54:36,760 --> 00:54:39,519
many variables and I just want to know everything about

898
00:54:39,559 --> 00:54:41,519
all of them, so it's hard for me to really

899
00:54:41,559 --> 00:54:46,119
pinpoint like what to tell people to ask. So I

900
00:54:46,119 --> 00:54:48,400
do think to a certain extent it does come down to,

901
00:54:48,800 --> 00:54:51,679
you know, personal preference of what you're wanting to get

902
00:54:51,719 --> 00:54:56,519
out of the experience. But yeah, I hope that answers

903
00:54:56,800 --> 00:54:57,320
a little bit.

904
00:55:01,159 --> 00:55:04,239
Speaker 4: And then a second question, and what ways do you

905
00:55:04,320 --> 00:55:08,000
feel the use of AI is beneficial to the Bigfoot

906
00:55:08,039 --> 00:55:09,719
community and where is it detrimental?

907
00:55:10,360 --> 00:55:14,000
Speaker 6: Yeah, so AI has actually been something I'm really interested in,

908
00:55:14,760 --> 00:55:16,840
very interested in AI and how to apply it to

909
00:55:17,079 --> 00:55:20,519
not only use tasquatch research, but just in general. I

910
00:55:20,559 --> 00:55:24,280
actually have a few videos on YouTube about this as well.

911
00:55:24,840 --> 00:55:26,920
How we can be applying it, how we shouldn't be

912
00:55:26,960 --> 00:55:30,440
applying it, which is almost more important than like how

913
00:55:30,480 --> 00:55:33,679
to apply it. The ways that I think it's going

914
00:55:33,719 --> 00:55:38,559
to be very beneficial is in its ability to kind

915
00:55:38,599 --> 00:55:43,800
of just make our make our work processes more efficient.

916
00:55:44,280 --> 00:55:47,480
So there's a couple of apps that I really like

917
00:55:47,559 --> 00:55:50,400
to use that are like text to speech and they

918
00:55:50,440 --> 00:55:53,880
also automatically grab like location data and time and like

919
00:55:54,039 --> 00:55:59,599
timestamps and whatnot. And you know, it obviously makes going

920
00:55:59,679 --> 00:56:04,079
through texts a lot easier if you know how to

921
00:56:04,159 --> 00:56:06,639
kind of mess with it, and it can really grab

922
00:56:06,679 --> 00:56:10,440
out information very well from text, especially depending on the

923
00:56:10,440 --> 00:56:13,480
model that you're using, you know, because different models are

924
00:56:13,519 --> 00:56:16,880
optimized for different things. Things we should not be using

925
00:56:17,519 --> 00:56:22,679
AI for is data analysis. We shouldn't be sending raw

926
00:56:22,760 --> 00:56:27,440
data right like through AI. And that's something that I'm

927
00:56:27,480 --> 00:56:30,880
starting to see and it's making me nervous because, like,

928
00:56:31,199 --> 00:56:33,719
at the end of the day, the thing that we

929
00:56:33,800 --> 00:56:37,039
call AI or just large language models, they're just text

930
00:56:37,039 --> 00:56:42,679
predators and so they can barely count like how many

931
00:56:43,320 --> 00:56:46,360
a's are in a word or whatever. Like if you

932
00:56:46,400 --> 00:56:49,920
ask it to count something, it's usually wrong. So you know,

933
00:56:50,039 --> 00:56:53,320
asking it to do data analysis is just going to

934
00:56:53,360 --> 00:56:58,000
be detrimental. Also, asking it to identify patterns in data,

935
00:56:59,159 --> 00:57:02,800
it's not very good at that either. So you know,

936
00:57:03,159 --> 00:57:05,480
things like that I think we need to be really

937
00:57:05,480 --> 00:57:09,800
careful about. But for the most part, it should just

938
00:57:09,840 --> 00:57:11,480
be a tool and the tool belt. You know, it

939
00:57:11,519 --> 00:57:14,800
shouldn't be driving our research and doing our research for us,

940
00:57:14,840 --> 00:57:19,480
but it should be a supplement. I personally think, yeah,

941
00:57:19,480 --> 00:57:21,159
that's a really good question. I could go on about

942
00:57:21,159 --> 00:57:24,320
AI forever because I love it and I'm like really

943
00:57:24,360 --> 00:57:27,320
getting into like how to how to do like AI

944
00:57:27,400 --> 00:57:29,000
engineering and stuff like that.

945
00:57:29,599 --> 00:57:37,800
Speaker 4: M gentlemen, any final thoughts or questions for Trestrial?

946
00:57:40,960 --> 00:57:44,519
Speaker 10: I was gonna say, we have, between Shan and I,

947
00:57:45,159 --> 00:57:48,519
we have a lot of witness reports. You know that

948
00:57:48,519 --> 00:57:55,960
would be a class A that may be usable for

949
00:57:55,960 --> 00:57:59,280
for the data set? Is that something that we could

950
00:58:00,079 --> 00:58:04,639
email to you? Uh, the information? And what format to

951
00:58:04,719 --> 00:58:07,239
you generally like to take that? Is it you know

952
00:58:07,320 --> 00:58:09,440
in Excel or or how Yeah?

953
00:58:09,760 --> 00:58:13,280
Speaker 6: So basically how I have things set up now my

954
00:58:13,719 --> 00:58:18,519
program that I have honestly a text file of the

955
00:58:18,679 --> 00:58:22,199
entire report for each report is the best. It depends

956
00:58:22,239 --> 00:58:25,800
on how how you haven't set up as is. But

957
00:58:26,159 --> 00:58:28,119
basically how my program runs is I just send in

958
00:58:28,159 --> 00:58:31,639
a report and it extracts, okay, all the information. So

959
00:58:32,519 --> 00:58:35,920
we can definitely chat about that.

960
00:58:36,599 --> 00:58:40,599
Speaker 4: We do have one question that's sneaked in from our

961
00:58:40,719 --> 00:58:43,639
executive producer, Brian Corbin. So we have to ask this question,

962
00:58:44,480 --> 00:58:48,599
and the question is can Terrestrial discuss what project she

963
00:58:48,679 --> 00:58:51,800
has that works with Darby Orchid at North Carolina State.

964
00:58:54,079 --> 00:58:57,280
Speaker 6: I don't think I'm at liberty just say quite yet,

965
00:58:59,400 --> 00:59:05,559
but yeah, maybe in another month or two I need

966
00:59:05,559 --> 00:59:09,239
to chat with him.

967
00:59:09,360 --> 00:59:12,360
Speaker 4: Very good, Tresture, Thank you so much for spending some

968
00:59:12,400 --> 00:59:15,360
time with us before you head out. Where can folks

969
00:59:15,400 --> 00:59:18,199
find out more about the Sasquatch Data Project and perhaps yourself?

970
00:59:18,760 --> 00:59:22,239
Speaker 6: Yeah, so I post the most on Instagram. My handle

971
00:59:22,280 --> 00:59:25,280
there is just at Sasquatch Data. It's actually my handle

972
00:59:25,320 --> 00:59:28,760
on everything is just at Sasquatch Data. But yeah, I'm

973
00:59:28,760 --> 00:59:33,239
on Instagram, Facebook, TikTok, YouTube, all that fun stuff. I

974
00:59:33,280 --> 00:59:35,920
try to post on YouTube like at least once a month,

975
00:59:36,000 --> 00:59:38,039
but on all my other channels I try to post

976
00:59:38,079 --> 00:59:41,719
weekly with data investigations and updates. You can also check

977
00:59:41,719 --> 00:59:45,519
out my research at Sasquatch Data Project dot com and

978
00:59:45,559 --> 00:59:47,840
if you want to reach out, my email is contact

979
00:59:47,880 --> 00:59:50,559
at Sasquatch Data Project dot com.

980
00:59:50,679 --> 00:59:52,840
Speaker 4: Very good, very good, Thanks so much for spending some

981
00:59:52,880 --> 00:59:58,639
time with us, folks, you much, Thank you so much. Folks,

982
00:59:58,639 --> 01:00:02,440
you've been listening to the Sasquatch experience here on Anomalous

983
01:00:02,599 --> 01:00:06,960
Entertainment on YouTube, Facebook, wherever you stream us like Lee,

984
01:00:07,320 --> 01:00:09,480
like us, subscribe to us if you want. I can't

985
01:00:09,519 --> 01:00:11,239
say it's that great to subscribe us all the time.

986
01:00:11,599 --> 01:00:14,679
But also rate us and review us. That's very important

987
01:00:15,400 --> 01:00:17,760
for us to get that feedback. And on that note, folks,

988
01:00:17,800 --> 01:00:20,239
thank you. We'll see you in two weeks. And for

989
01:00:20,440 --> 01:00:22,039
those of you that are going to join us at

990
01:00:22,039 --> 01:00:25,000
the Pennsylvania Camping Adventure, we look forward to hanging out

991
01:00:25,000 --> 01:00:28,280
with you. Thank you again. And hey, one more thing,

992
01:00:28,440 --> 01:00:31,760
Squatch out hunger folks, Sasquatch Experience dot com. Click on

993
01:00:31,800 --> 01:00:34,840
the banner, help us feed some people and we'll see

994
01:00:34,840 --> 01:00:41,360
you next time. You've been listening to The Sasquatch Experience.

995
01:00:41,880 --> 01:00:49,960
Please rate and review wherever this podcast is consumed. For

996
01:00:50,039 --> 01:01:05,960
more information, please go to our website, Sasquatch Experience dot com.

997
01:01:06,639 --> 01:01:07,920
Keep on watching