Spatiotemporal patterns in British racing and equestrian sports: Implications for pathogen transmission
Bibliographic record
Abstract
BACKGROUND: The widespread assumption that there is minimal potential for pathogen transmission between British racehorse and sport horse populations remains unverified by empirical evidence. OBJECTIVES: To characterise spatiotemporal patterns of horse attendance at racing and other sport events in Great Britain in 2018. STUDY DESIGN: Spatiotemporal analysis. METHODS: Publicly available data from British Horseracing Authority, British Dressage, British Eventing, Endurance GB, and British Showjumping events in Great Britain during 2018 were analysed. Horse attendance was summarised by discipline, month, and season. Venue density was mapped with kernel density estimation. Sport venues within 5 km of racecourses with horse attendance within 24 h of racing were identified and Kulldorff's spatial scan statistic was used to detect significant time-space clustering of venue use. RESULTS: Excluding showjumpers, 49,012 horses competed in 8314 events across 598 venues during 2018, generating over 400,000 horse-venue attendances. Most horses (97.2%; n = 47,635/49,012) competed in a single discipline. Venue attendances peaked in summer and were concentrated in southeast England. There were five significant space-time clusters of venue-events within 5 km and 24 h of each other involving 5 racecourses and 8 sport venues. The most likely cluster was in the southeast of England, between January and July, with a relative risk of 62.54. MAIN LIMITATIONS: Inconsistent horse identification precluded horse-level analysis of showjumping data. CONCLUSIONS: Racehorse and sport horse populations competing in Great Britain are largely separate, but limited opportunities for local or indirect pathogen spread do exist during peak seasons in areas with high venue density.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".