Effects of space allowance on behaviour during lairage, stress physiology, skin lesion scores, and meat quality of market-weight pigs transported in an actively ventilated vehicle in the summer
Bibliographic record
Abstract
This study aimed at assessing the effects of space allowance on microclimate and pig stress during transportation in a mechanically-ventilated vehicle. On each journey (6), 114 pigs were randomly assigned to different space allowances (0.46, 0.49, or 0.59 m2/pig) tested in the near-front, middle, and near-rear compartments and on the top, middle, and bottom decks. In each compartment, ambient conditions (e.g., T °C and RH %) were monitored. Behaviour was recorded during lairage. A total of 162 pigs were selected for the analysis of blood hematocrit proportion and lactate, and creatine kinase (CK) concentrations, and the evaluation of meat quality in the loin and ham muscles. The bottom deck was more humid before and during transport ( P ≤ 0.05) and pigs from this location stood and drank more in lairage ( P < 0.001). Pigs transported on the top deck had greater blood lactate and CK concentrations ( P < 0.05) than those transported on the bottom deck, and when transported at 0.49 m2/pig produced paler loins ( P < 0.05) than those transported at 0.46 m2/pig. The within-trailer location had a greater impact on the microclimate, collected animal welfare measures, and meat quality of pigs transported in an actively ventilated trailer than space allowance.
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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) | 0.001 | 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".