Bibliographic record
Abstract
After my exam for fellowship of the Royal College of Anaesthetists (FRCA) I decided to do a 12 month clinical research fellowship in Toronto, Canada. I left for the frozen north with an interest in ambulatory anaesthesia, a need to spice up the back page of my curriculum vitae, and a longing for off piste skiing. As it turned out, my year included the hottest Canadian summer and the coldest winter in living memory, two operations to rebuild a broken leg, a new cold war, the largest outbreak of severe acute respiratory syndrome (SARS) in the Western world, West Nile virus, and no skiing whatsoever. Toronto means “the meeting place” in native North American Indian, and it's certainly a buzzing cosmopolitan city. I worked with other research fellows from every corner of the globe. The Toronto Western Hospital is the largest combined neurosurgical and spinal surgical centre in North America. It has …
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".