The effects of driving events on the stability and resting behaviour of cattle, young calves and pigs
Bibliographic record
Abstract
Abstract The welfare of animals in transit may be affected by driving events, such as acceleration, braking and cornering. The relationships between driving events and the behavioural responses of the animals were examined. A single-deck, non-articulated vehicle was fitted with a video-recording system, GPS and tri-axial accelerometer. Two drivers each drove three standard journeys (two 3-h stages on different types of roads) for each animal type. Six different groups of five cattle(Bos taurus), ten calves and ten pigs(Sus scrofa)were each transported on separate journeys. Cattle stood still for most of each journey. Calves spent more time lying down during the second stage of the journey than during the first. Although pigs spent some of the time lying down, they spent more time sitting down and this time was greatest on a motorway and during the second stage of the journey. Frequent adjustments to maintain stability were required in response to acceleration, braking, cornering and rough road surfaces. Some animals experienced repeated falls. Falls occurred after a series of different types of events. The fewest losses of balance occurred on the motorway. As a motorway is a limited access multi-lane carriageway not crossed on the same level by other traffic lanes, the driver does not normally undertake frequent vehicular adjustments to respond to road features. Therefore, motorways give animals an opportunity to rest and avoid discomfort from repetitive driving events. If drivers anticipate potential driving events and prepare for them, it will reduce the likelihood and severity of losses of stability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".