Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.009 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it