Princess Diana and Reduced Traffic Deaths in France and the United States
Bibliographic record
Abstract
Princess Diana and Reduced Traffic Deaths in France and the United States Affiliation Donald A. Redelmeier MD, MSHSR, FACP, and Junaid A. Bhatti MBBS, MSc, PhDDonald A. Redelmeier is with the Department of Medicine, University of Toronto, and the Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada. Junaid A. Bhatti is with the Institute for Clinical Evaluative Sciences and the Department of Surgery, University of Toronto.CopyRightCorrespondence should be sent to Donald A. Redelmeier, Sunnybrook Health Sciences Centre, G-151, 2075 Bayview Ave, Ontario, Canada M4N 3M5 (e-mail: [email protected]on.ca). Reprints can be ordered at http://www.ajph.org by clicking the "Reprints" link.Note. The views expressed are those of the authors and do not necessarily reflect those of the Ontario Ministry of Health & Long-Term Care.CONTRIBUTORSD. A. Redelmeier wrote the first draft of the editorial. Both authors contributed to revisions and the final decision to submit for publication. https://doi.org/10.2105/AJPH.2017.303880 Accepted: April 28, 2017 Published Online: July 12, 2017
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.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".