Descriptive network analysis of Ontario, Canada equine competitions: implications for disease control
Bibliographic record
Abstract
BACKGROUND: Competitions are an important source of entertainment and revenue in the horse industry but may contribute to disease introduction and spread. The objectives of this study were to, (i) describe the annual (2016 to 2018) contact networks of Equestrian Canada competitions in Ontario, Canada, and (ii) determine if the networks exhibit characteristics of 'small world' networks. Data on Equestrian Canada registered competitions in the province of Ontario, Canada between 2016 and 2018 were used to create three types of yearly contact networks: competition networks, horse networks, and venue networks. RESULTS: Dressage, hunter/jumper, and eventing competitions were connected through horses co-attending the same competitions; however, endurance and reining shows were isolates in these networks. The median node degrees in the yearly horse networks were between 567 and 619 with wide variation in node centrality scores. Horses competing in multiple disciplines at multiple levels had high node betweenness scores. Horse networks and venue networks had similarly short geodesics as random Erdös-Renyi networks of the same size but exhibited higher levels of clustering indicating that both the horse and venue networks meet the criteria for 'small world' networks. CONCLUSIONS: The high connectivity of the networks may provide opportunities for disease transmission to occur between competition levels and disciplines, and potentially increase case counts in an epidemic. The 'small world' topography of the competition and venue networks means disease spread could occur more rapidly in this population and the threshold for disease persistence may be lower.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".