Episode #65: BACK FROM THE MONTH OFF- Roasted by Jimmy Skinner
Bibliographic record
Abstract
The podcast where we get personal with notable Winnipeggers Nicolas Bueno and Kanen Ling are back this week with a brand new  episode of  Winnipeg's  Finest! Today's guest is comedian Jimmy Skinner, who roasts both Kanen and Bueno for whatever reason, talks about a fantastic new social, winning a stuffed animal at socials, his favourite sports teams and why the Raptors are better than the Sacramento Kings, awkward comedy situations, and so much more. Catch a   new episode every   Monday  at 7PM!   We  are growing so  fast and    appreciate every single  fan  and  act  of   support and   appreciation! Make sure to get more content by following us on social media: Twitter and Instagram: @wpgsfinestpod JELLYFISH FLOAT SPA: CODE FOR 15% OFF EVERY FLOAT: "wpgsfinest"              Instagram: @jellyfishfloatspa Twitter: @floatwinnipeg  website: jellyfishfloatspa.com       (204) 294-9890 894 St. Mary's Interviewee's social media: Instagram: @skinnerdoescomedy Info on new comedy show at Underdog's: @jessolido BEATS: Kav Gandhi: (https://soundcloud.com/kavgandhi) --- Support this podcast: https://podcasters.spotify.com/pod/show/nicolas-bueno/support
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.138 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".