7Redi (Swiss Knife YouTube Reviewer) - The Knife Junkie Podcast Episode 288
Bibliographic record
Abstract
Swiss Knife YouTube Reviewer 7Redi joins Bob "The Knife Junkie" DeMarco on episode 288 of The Knife Junkie Podcast. Find show notes and links for this episode at https://theknifejunkie.com/288. 7Redi is a YouTuber from Switzerland who collects and reviews folding knives, EDC gear, pens, wallets, tools, prybars and fixed blades. And by the way, he has an amazing collection of folding knives as well as fixed blades too. 7Redi wants to be a helpful resource for knife enthusiast in Switzerland and Europe with advice and reviews. He also wants to give U.S. and Canadian viewers on his YouTube channel some insight into Swiss gun and knife ownership. You can find 7Redi on YouTube at https://www.youtube.com/channel/UCsMH7ph-uPDPOQiq_Ocm5cw and on Instagram at https://www.instagram.com/7redi_knife_reviews/. Be sure to support The Knife Junkie and get in on the perks of being a Patron -- including early access to the podcast and exclusive bonus content. Visit https://www.theknifejunkie.com/patreon for details. Let us know what you thought about this episode. Please leave a rating and/or a review in whatever podcast player app you're listening on. Your feedback is much appreciated. Also, call the listener line at 724-466-4487 or email bob@theknifejunkie.com with any comments, feedback or suggestions on the show, and let us know who you'd like to hear interviewed on an upcoming edition of The Knife Junkie Podcast. To listen to past episodes of the podcast, visit https://theknifejunkie.com/listen.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.623 | 0.008 |
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".