Bibliographic record
Abstract
No AccessUrology PracticeBusiness of Urology1 Jul 2018Editorial Commentary Daniel Liberman and Luc Valiquette Daniel LibermanDaniel Liberman More articles by this author and Luc ValiquetteLuc Valiquette More articles by this author View All Author Informationhttps://doi.org/10.1016/j.urpr.2017.06.008AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Editorial Commentary." Urology Practice, 5(4), p. 278 References 3 : Prescription opioid misuse in the United States and the United Kingdom: cautionary lessons. Int J Drug Policy2014; 25: 1124. Google Scholar 4 : Prospective double-blind preoperative pain clinic screening before microsurgical denervation of the spermatic cord in patients with testicular pain syndrome. Pain2014; 155: 1720. Google Scholar © 2018 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 5Issue 4July 2018Page: 278 Advertisement Copyright & Permissions© 2018 by American Urological Association Education and Research, Inc.MetricsAuthor Information Daniel Liberman More articles by this author Luc Valiquette More articles by this author Expand All Advertisement PDF downloadLoading ...
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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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.104 | 0.045 |
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".