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By Jove, I think you've got it.

"I do believe there's a significant possibility of there being a hoaxer in these woods." Just my opinion and totally understanding that others could conclude otherwise (if you were drunk or high). And I do believe you have correctly identified the mask used. I'm guessing that boot black was applied to the mask to make the face more consistently dark. Listening to the video (so others don't have to), the purported pictures aren't even shown until around the seven minute mark and the "witness" is having a conversation with "Nef" (so recording audio) but only taking stills, not videotaping so that the super-true, really lifelike movement (of people rearranging the props) can be seen. Amazing he has 80,000 subscribers on YouTube. And very sad.

I still check BFF occasionally to see if there are any recent topics of interest. However, a lot of the material and debates in BFF seem to go through repeat cycles. For field research topics of interest and in my region (CA, OR, and WA), I have moved on to Facebook and interact with non-anonymous field researchers in private Facebook groups. I find it more rewarding to deal with folks who I have actually met in the field, many who have more years of field experience and/or that have focused on particular aspects (like audio recording or thermal imaging or other technical aspect) and are willing to share their expertise.

I would create a separate variable to code a report as either a likely hoax or not, so that I could see at what level hoaxes are happening most frequently. There are at least 2 possible forms of hoaxing when it comes to witness reports. One is the witness as the perpetrator of the hoax and the other is the witness as the victim of a hoaxer. For example, did the witness create the fake print, or is the witness playing into the hand of a hoaxer? I suspect this latter scenario is very difficult to confirm. I suppose there's also the "accidental hoaxer" -- that Bigfoot researcher banging on trees, making Bigfoot calls, and scaring the hell out of nearby campers who subsequently submit a report to the BFRO!

Looks pretty cool and seems like it would scrape reports up to the current date. The python coding is beyond my capabilities, sadly. Other datasets I've reviewed all include the BFRO data but not all the variables and not reports from the last couple years. The most comprehensive is the SSR dataset with the BFRO reports, John Green reports, and reports from various regional Bigfoot research groups. One question I had of the BFRO data was about the types of interactions witnesses were reporting. David Daegling in his book Bigfoot Exposed argued that most Bigfoot encounters are "mundane" -- the witness sees a Bigfoot, there's a brief period of mutual recognition, and then the Bigfoot just walks away. This certainly wasn't my impression. To get a sense of what witnesses are reporting, I pulled a random sample of BFRO reports of encounters that happened between 2010 and 2022 and then read and coded the witness descriptions (this is the "observed" variable in the BFRO dataset). I created the following coding scheme: Level-0: These are asynchronous encounters. That is, there’s evidence that a Bigfoot might have been in the witness’s current location, as shown by foot tracks, scat, stray hairs, tree breaks, etc. [this would map to the BFRO Class C] Level-1: This and the remaining levels are synchronous encounters. In Level-1 there is evidence of Bigfoot currently in the witness’s proximity, as demonstrated by loud calls, tree knocks, stone throws, strong odors, bi-pedal footsteps, etc. Bigfoot is believed to be nearby but there is no visual confirmation. [this would map to BFRO Class B] Level-2: This adds visual confirmation but the sighting is one-directional. There is no indication from the witness that the Bigfoot was aware of the witness’s presence. [this gets into BFRO Class A but depends on observability] Level-3: This introduces mutual recognition between the witness and the Bigfoot. The Bigfoot simply acknowledges the witness and then casually turns away and disappears into the forest. "Mundane". [this seemed to be what Daegling wrote about; the P-G encounter would fit here] Level-4: These are aggressive interactions with Bigfoot, as demonstrated by Bigfoot bearing teeth, chest pounding, yelling or roaring, charging, or similar behaviors targeted at the witness. Level-4 is often characterized by competition between the witness and Bigfoot, whether for home territory, hunting grounds, or specific prey animals. [I would put Mike Wooley's encounter and Wes Germer's encounter here, as examples] Turns out most encounter reports fall into Level 1 and Level 2... and most of these cases are susceptible to alternative explanations and readily dismissed by debunkers. I suspect few even consider filing a report for Level-0, though cases like Cripple Foot and the Skookum Cast fit here. Level-3 cases were infrequent (~10% of cases) and Level 4 non-existent. Here's how my sample of 102 cases breaks down: overall (top chart) and by BFRO's Class A and Class B.

My name is Shelley and I recently had an encounter on a BFRO expedition. I have always been interested in this subject, but this experience has motivated me to learn as much as I can about this subject. I appreciate being added to the Bigfoot Forum and I hope to learn from some of the experienced researchers here. Cheers!

Computer guy here. High scale, petabytes scale, and higher. Tens of thousands of servers and hundreds of thousands of "containers" scale. And of course loads of old and recent machine learning and AI experience. It's a tool. It's fed poorly, limited information from limited sources, sources that are biased towards certain answers and limited in many ways. I am curious as to what it would regurgitate if it were trained in all US government archives, like one had access to a trove of secret documents. For example, I use an SDK or software agent to deliver log files and metrics to time series databases, that SDK and agent have libraries written for it. When asked how to plumb up certain technologies with the SDK, some AIs will make up answers out of whole cloth, faking a reply. For me it's not a huge deal because the libraries are protected by many layers but for other tools a bad actor could write that fake library and use it for compromising systems. Unreliable. But teach it to only reply with proven libraries with digital signatures signed by some author and your answers improve. I suspect that if we ever let it in to truly hidden archives, the secrets it would out would far sooner start WW3 than expose a cover-up of cryptids, but that's the only way something like this would be revealed by AI. We would have to plumb the archives of a dozen agencies to get access to the mundane reports from the field and even then have to piece it together ourselves. Archives that won't be made public anytime soon. The current language models are trained on mundane stuff, unfortunately. But, someday, you'll stumble into some university archives only accessible via secret username and password, and pass that login information and URL to an LLM and feed and prompt it, here's a URL and the login ID is Joe, password is password123, digest all you can find and summarize the reports mentioning Bigfoot, Sasquatch and any other similar species... And only then will it start to get fun. The question is who lets one in first and who writes the first prompt to expose it.

I'm a believer in Sasquatch, and i hope to someday become a knower but I asked chatgpt if Bigfoot is real and this is what it said: is bigfoot real? ChatGPT said: No, Bigfoot is not real. There is zero scientifically credible evidence to support the existence of such a creature. Every supposed sighting, footprint, or video has either been debunked, lacks verifiability, or is anecdotal. In contrast, extensive wildlife studies, camera traps, and modern tracking technologies have failed to produce a single verifiable specimen or consistent biological trace. The idea persists because it's profitable (TV shows, merchandise, tourism) and taps into human fascination with the unknown. But from a practical standpoint, a large, elusive primate population surviving undetected in North America is implausible. It's fiction, not fact.

This caught my attention, since my topic post w/ graph showed an observable downward trend. Looking back on my data, code, and graph, I discovered I had mistook the date field as the submission date, when in fact what the Kaggle author called 'timestamp' is actually the reported sighting date. I should have caught this. The submission date is not available in the dataset I had used. The trends that AI pulled from Reddit are based on what the Redditor called an updated dataset relative to the one I used. This updated version has a submission date and a messy sighting year field (e.g., 2022, 2014-ish, 2001-2002, 1987 and 1994, 2011, etc.). The updated version also cuts off at 2021. There are other differences between the datasets, but here's what I found in terms of AI's response: Yes, there was a spike in 2012, though these were largely Class B sightings. My guess is that this comes from heightened awareness from Finding Bigfoot, which premiered in 2011. The downward trend resumed its course after the spike in 2012. Yes, there was an upward trend but it reversed around 2005. Here's my updated graph with correct labeling (LEFT) and a graph I created from the 'updated' data linked by the Redditor (RIGHT). Note that i had fewer records to graph (on the left), as I removed any records missing a date/year value (due to the witness unable to recall the encounter date). The graph on the right, since it's using the actual submission date (rather than the encounter date), had far fewer missing values (roughly 1000 more records to graph). BFRO launched in mid-late 1990s, and this is reflected in the near-zero submissions prior to then (righthand graph).

Sasquatch Ontario, ran by Mike Paterson, released a YouTube video two years ago titled 'The Face of Nef', which featured a photo he took of an alleged sasquatch. Here's the photo in question: Even though I believe this is an obvious hoax due to the static facial expressions and the features of the subjects having that cheap material quality to them that your average modern-day Halloween mask has, I still wasn't satisfied. I've actually been looking around for a while now and I'm almost positive I found the masks used in this photo. I managed to find this listing on Facebook Marketplace of a Halloween mask that looks exactly like the face of the subjects in the 'Face of Nef' photo. Here's the mask I found: Here's a comparison of the two: Additionally, this mask that I found appears that it was listed near Creemore, Ontario, the same province where Mike Paterson lives. I know the common consensus is that this photo was hoaxed, but I hope this helps to confirm it anyways.

Why do you say that? And do you disagree with my post at all?