Risk factors for bruising and mortality of broilers during manual handling, module loading, transport, and lairage
Bibliographic record
Abstract
Multiple factors can affect the risk of bruising and mortality of broilers during loading, transport, and lairage. The risk factors affecting the percentages of broilers in each load that were “dead-on-arrival” (DOA) or bruised were studied from records provided by a processing plant, by undertaking direct observations during on-farm loading and then carrying out multivariable analyses. Selected loads between 2014 and 2015 from seven producers were included in the study. The median DOA per load was 0.13% (Q 1 = 0.06, Q 3 = 0.25, n = 212), the median total duration from loading to unloading was 8.6 h, and the external temperature ranged from −22 to 22 °C. Although it was not possible to adequately characterise thermal conditions within each load, the analysis indicated that the main risk factors for increased mortality were in spring and winter, an increased duration between loading and end of lairage, and a period of feed withdrawal before loading longer than 6 h. The risk of mortality increased with the weight of the birds and with an increase in rearing mortality. No relationships were found between the manner in which the broilers were handled and the percentages of DOAs or bruised birds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".