6 Does Bedding Influence Lying Behaviour of Cattle Unloaded for Rest During Long-distance Transportation?
Bibliographic record
Abstract
Abstract In Canada, cattle must be unloaded, fed, watered, and rested after 36 h of transport; however, little is known about what constitutes appropriate rest station conditions. The objective of this study was to determine the relationship between providing straw bedding (14-cm deep) and trip, load, and commercial rest station characteristics, on lying behaviour. Truckloads (n = 13) were split; half the animals were assigned to either bedded (n = 452 cattle) or non-bedded (n = 470 cattle) pens. Trip characteristics [time in motion (TIM), duration of stops en route (DUR_STOPS)], load characteristics (sex: heifers, steers, both; and load weight), and rest station characteristics [ambient temperature at unloading; rest pen space allowance (k_PEN); time in resting pen (TIP)] were recorded. Once unloaded we counted the number of cattle lying/pen, every 10 min for 8 h. A mixed logistic regression model with random intercepts for load and truck compartment was fitted to examine associations between the proportion of animals lying and the independent variables (i.e., treatment, trip, load, and rest station characteristics). Odds of lying increased with load weight (i.e., cattle weight class, P = 0.02) and with DUR_STOPS (P < 0.03). There was an interaction (P < 0.01) between treatment and TIM: as TIM increased, the odds of observing cattle lying showed a notable increase for cattle rested in bedded pens, whereas for those rested in non-bedded pens, the odds showed little change as TIM increased. An interaction (P < 0.01) was also found between treatment and TIP: early in the observation period, the odds of cattle lying were greater in bedded pens. Both groups showed an increase in the probability of lying over time, plateauing at similar levels, near the end of the 8-h observation period. In conclusion, providing straw bedding at rest stations influenced cattle’s motivation to lie, particularly following longer transport durations.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".