Bibliographic record
Abstract
Graves et al. Respond Affiliation Janessa M. Graves PhD, MPH, I. Barry Pless MD, and Frederick P. Rivara MD, MPHJanessa M. Graves is with the College of Nursing, Washington State University, and the Harborview Injury Prevention and Research Center (HIPRC), Spokane, WA. I. Barry Pless is with McGill University, and the Injury Prevention Program, Montreal Children’s Hospital, Montreal, Québec. Frederick P. Rivara is with Seattle Children’s Hospital and the University of Washington, Seattle. Frederick P. Rivara is also with HIPRC.CopyRightCorrespondence should be sent to Frederick P. Rivara, MD, MPH, Harborview Injury Prevention and Research Center, Box 359960, 325 Ninth Ave, Seattle, WA 98104 (e-mail: [email protected]edu). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link.ContributorsAll authors contributed equally in all aspects of authorship of this letter and approved the final version submitted for publication. https://doi.org/10.2105/AJPH.2014.302215 Accepted: July 09, 2014 Published Online: October 08, 2014
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.080 | 0.034 |
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".