Pushing the Limit: How to Get the Most Out of Cold Snares
Bibliographic record
Abstract
1Division of Gastroenterology, Centre hospitalier de l'université de Montréal (CHUM) and Research Center (CR-CHUM), Montreal, Quebec, Canada 2Section of Gastroenterology, White River Junction VA Medical Center, White River Junction, Vermont, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA Correspondence: Daniel von Renteln, MD, Division of Gastroenterology, Centre hospitalier de l'université de Montréal (CHUM) and Research Center (CR-CHUM), 900 Rue Saint-Denis, Montreal, Quebec H2X 0A9, Canada. E-mail: [email protected] Guarantor of the article: Daniel von Renteln, MD. Specific author contributions: Daniel von Renteln and Heiko Pohl: drafting and revision of the manuscript. All authors have approved the final manuscript submitted. Financial support: None. Potential competing interests: Heiko Pohl is a consultant for Interscope Inc. No conflict of interest exist for Daniel von Renteln. SUPPLEMENTARY MATERIAL accompanies this paper at https://links.lww.com/AJG/A750
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.042 | 0.035 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".