<scp>IFHA Global Summit</scp> on <scp>Equine Safety</scp> and <scp>Technology</scp> : Reducing the risk of <scp>Exercise Associated Sudden Death</scp>
Bibliographic record
Abstract
In June 2024, an international multi-disciplinary group of researchers and clinicians with an interest in Exercise Associated Sudden Death (EASD) gathered at Woodbine Racecourse, Toronto.The aim was to discuss current evidence in the field, identify knowledge gaps, and suggest potential pathways to solve those gaps.A critical goal was to discuss how new and evolving knowledge and technology can be harnessed to provide tangible and practical improvements to equine safety and welfare.The workshop was included in the International Federation of Horseracing Authorities (IFHA) Global Summit on Equine Safety & Technology, an event sponsored by The Hong Kong Jockey Club Equine Welfare Research Foundation, Cornell University's Harry M Zweig Memorial Fund for Equine Research and Woodbine Entertainment Group.The discussions aimed to form a multi-disciplinary group of experts that could act as advisors and critical friends to racing, providing a unique opportunity to forge collaborations and open conversations that will lead to practical, actionable items for future implementation.In the face of changing societal perspectives on the use of animals in sport, racing's social licence to operate is truly under threat.Consequently, the industry and governing bodies need to demonstrate to the public that equine welfare is being taken seriously and forge a path to ensure continued acceptance of racing.The research community must gather the data needed to support the ongoing development and adoption of evidence-based strategies to reduce fatality rates.This editorial serves to highlight the key areas of discussion along with outcomes to be actioned by the group, most notably the need to determine what is 'normal' in terms of cardiopulmonary physiology and the identification of risk factors for EASD.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.081 | 0.015 |
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".