Effects of space allowance on behaviour during lairage, stress physiology, skin lesion scores, and meat quality of market pigs transported in an actively ventilated vehicle in the winter
Bibliographic record
Abstract
A total of 1488 pigs were transported to slaughter using a mechanically ventilated vehicle in the winter in Canada. On each of six journeys, a sub-sample of 78 pigs were randomly assigned to two space allowances (0.46 or 0.53 m2/pig), distributed across six compartments, in two positions (near-front and near-rear) and three deck levels (top, middle, and bottom). Compartment ambient conditions (e.g., T °C and RH %) were monitored during transport, and pig behaviour was recorded during lairage. Blood lactate, hematocrit, creatine kinase (CK) concentrations, and meat quality in the longissimus (LM), semimembranosus (SM), and adductor (AD) muscles were assessed on a total of 108 pigs. Pigs transported at 0.53 m2/pig had greater blood hematocrit levels ( P = 0.05), but lower blood CK concentrations at slaughter ( P = 0.01). The top deck was colder during all transport events ( P < 0.001), and pigs transported in this location stood less in lairage compared to those transported on the bottom deck ( P = 0.05). Pigs from the top deck showed lower lightness (L*) values in the LM and SM muscles ( P = 0.02 and 0.04, respectively). Overall, animal location had a greater impact than space allowance on animal welfare and meat quality of pigs.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".