StrathAyr Horse Health Discussion Document.
Bibliographic record
Abstract
My interest in the prevention of Racetrack Fatalities and Injuries dates back approximately 25 years from when I was based in the United Kingdom at the University of Bristol. Links that I established in the United Kingdom were maintained when I moved to Canada in early 1990’s and it was from Canada that I worked on a project looking at racetrack fatalities with Sharon McKee for the British Horse Racing Industry. On returning to Melbourne in 2000 as Chair of Equine Studies I was charged with developing a project to examine risk factors associated with racetrack fatalities for racehorses in Victoria. I was the principal investigator on a project entitled “Epidemiology and risk factor analysis of racetrack fatalities”. The aim of the study was to identify risk factors associated with racetrack fatalities in Victoria. We put together an impressive team of researchers from Australia and overseas and Dr Lisa Boden joined the project as a PhD student. It was Lisa’s hard work that was pivotal to the success of the project. The study included an ongoing study of post-mortem examinations of horses that died on the racecourse as well as a retrospective study of historical records of racetrack fatalities recorded in Victoria between 1989 and 2004 (Boden et al 2006). The data
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.607 | 0.208 |
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".