How Important are Best Practices and a Horse’s Characteristics to Protect Welfare During Equine Air Transport?
Bibliographic record
Abstract
Abstract Equine air transport is a complex event. This study presents the cases of two horses transported from the USA to New Zealand and discusses the importance of knowing the horse’s temperament and previous travel history (i.e., transport-related health and behavioural problems) and appropriate management. The two horses were reported to have temperament traits of nervousness/excitability and stubbornness, limited travel training and experience, and previous transport-related problems. After being quarantined for 23 days, the horses were transported by road to the departure airport, where they were unloaded from the road vehicle, health-checked, and loaded without problems into three-horse capacity jet stalls, by a flight groom with 50 years of experience in horse handling. During the flight, the horses were regularly checked, watered, and fed; both ate and drank. However, on arrival, when the horses were monitored, both showed fever (rectal temperature > 38.6°C). While the flight groom’s experience may have facilitated the handling of the horses during the air transport phases, reducing the risk of injuries, the horses could have had difficulty coping with air transport stress, developing fever after the flight due to their poor temperament and previous travel history. Knowing the individual details of the horses, training or re-training them for loading and travelling, and monitoring them carefully before, during, and after the air journey are recommended to minimise the welfare issues associated with air transport. Information © The Authors 2024
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".