Bibliographic record
Abstract
Extract I want to acknowledge the many friends who have challenged me and contributed to my learning and continuing education during my professional life. I feel particularly indebted to many role models and teachers including Olive Scott and Fergus Macartney from Killingbeck Hospital Leeds, Dick Rowe and Bob Freedom from The Hospital for Sick Children Toronto, Vera Aiello from The Heart Institute, Sao Paulo Brazil, Anton Becker from The Academic Medical Centre Amsterdam and Derek Gibson, Graham Miller, Elliot Shinebourne, Bob Anderson, Jane Sommerville, Chris Lincoln, Darryl Shore, Jan Till, Yen Ho, Sabine Ernst, Michael Gatzoulis, Piers Daubeney and Julene Carvalho from the Royal Brompton Hospital London. Thank you to the many cardiologists, surgeons and fellows not only from the UK, but also from Brazil, Argentina, Chile, USA, Canada, Europe, Australia and Asia who have spent time at the Royal Brompton and stimulated me to continue to learn and to teach....
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.233 | 0.193 |
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".