PSXIII-1 Does Providing Bedding Change the Latency and Duration of Cattle Lying Behavior During Long-Distance Transport Rest Stops?
Bibliographic record
Abstract
Abstract We explored whether straw bedding at rest stations might affect latency and duration of lying down beyond the 8h rest required at rest stops during long distance transport. Animals arriving to commercially operated rest stops (n=75, 6/load, opportunistically selected) were rested in pens (15.5 × 9.5 m) that were either bedded (n=38, straw, 14 cm deep) or non-bedded (n=37). The lying activity of each animal was recorded every 10 min for 8 h. The independent variables recorded included: bedding treatment, mean animal weight/load (kg), and space allowance [k-value = (m2/animal) / (BW2/3)] in the trailer. Ordinary linear and mixed linear regression models were fitted to assess lying latency and duration, respectively. Bedding affected latency to lie down, but its effect depended on space allowance in the truck: among cattle transported with low space allowance (2.08 - 3.29 m2/ 300 kg animal), bedded cattle laid down sooner than non-bedded cattle (P< 0.001). Comparing only cattle in bedded pens, cattle laid down sooner when transported with low space allowance (2.08 - 3.29 m2/ 300 kg animal) compared with medium space allowance (>3.29 – 3.69 m2/ 300 kg animal; P=0.003). Bedding also affected lying duration, but the effect depended on mean animal weight; as mean animal weight of the load increased so did duration but the effect was greater among bedded animals (P=0.027). In summary, cattle transported at high stocking densities are most likely to benefit from bedding as are heavier animals.
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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 | 0.000 |
| 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) | 0.003 | 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".