PSXIII-2 Effect of Rest, Post-Rest Transport Duration, and Conditioning on Performance and Physiological Welfare Indicators of Beef Calves
Bibliographic record
Abstract
Abstract The aim of this study was to assess the effects of conditioning, rest, and post-rest transport duration on welfare indicators of 6-7 mo old beef calves. A total of 328 weaned calves (237 ± 29.7 kg BW) were randomly assigned to a 2 × 2 × 2 factorial design: conditioning, conditioned (C) or non-conditioned (N); rest, 0 (R0) or 8 (R8) h, and post-rest transport, 4 (T4) or 15 (T15) h. Calves were sampled prior to and after 20h of transport, prior to and after the additional 4 or 15-h transport, and at 1, 2, 3, 5, 14, and 28 d after transport ended. Data were analyzed using the GLIMMIX procedure of SAS. Fixed effects were conditioning, transport and time nested within rest period, while random effects were animal and pen. For R0-T4 calves, the mean L-lactate concentrations were greater than R8-T4 calves on d 1 and 2 (p-values = 0.02) while, for R0 calves, mean ADG was greater than R8 calves 14 to 28 d after transport (p-values < 0.01). For R8-T4 calves 1 week after transport, mean ADG was greater and mean WBC counts were less than R8-T15 calves on d 5 (p-values < 0.01). For N calves, overall mean haptoglobin, creatine kinase, serum amyloid-A, and non-esterified fatty acids were greater than C calves (p-values ≤ 0.05). Overall, few and inconsistent differences were observed for rest, where rest improved L-lactate but affected ADG. Few differences were observed for transport, where shorter transport durations after the rest improved ADG and WBC counts. In general, N calves had greater physiological indicators of reduced welfare than C calves. Based on these findings, the best way to improve calf welfare during and after transport is to condition them prior to transport.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".