Simulated transport of well- and poor-feathered brown-strain end-of-cycle hens and the impact on stress physiology, behavior, and meat quality
Bibliographic record
Abstract
Transportation of poultry is stressful, especially for end-of-cycle hens (EOCH) experiencing metabolic stress. The aim of this study was to evaluate the effects of simulated transport on well- and poor-feathered brown-strain EOCH. The study (5 × 3 × 2 factorial arrangement) consisted of 5 temperature and relative humidity (RH) combinations applied directly at crate level (−10°C uncontrolled RH [−10], +21°C 30%RH [21/30], +21°C 80%RH [21/80], +30°C 30%RH [30/30], or +30°C 80%RH [30/80]), 3 durations (4, 8, or 12 h), and 2 feather covers (well [WF] or poor [PF]). Hens (n = 540) from 3 commercial farms were housed for a 3- to 5-d adaptation period, then feed was withdrawn before treatment exposure (crate density 54.5 kg/m2). Data collected included chamber conditions, feather condition score, behavior, blood physiology, core body temperature, mortality, and meat quality. Data were analyzed (randomized complete block design) using ANOVA; significance declared at P ≤ 0.05. Time spent performing thermoregulatory behaviors increased for hot (30/30 and 30/80) and cold (−10) treatments. Mortality only occurred in hens exposed to −10 and increased with longer duration. Cold exposure impacted meat quality, resulting in higher thigh pH and lower L∗ (lightness) and b∗ (yellowness). Prolonged exposure duration resulted in dehydration, indicated by blood physiology (hematocrit and hemoglobin) and live shrink. PF hens struggled with thermoregulation in −10, while WF hens struggled in 30/30 and 30/80. These results demonstrate that EOCH exposed at crate level to hot (+30) conditions experience thermal stress, while hens exposed to cold (−10) are unable to cope, compromising welfare and meat quality.
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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".