Rearing and handling injuries in broiler chickens and risk factors for wing injuries during loading
Bibliographic record
Abstract
Some injuries to broilers occur during rearing, but most injuries occur during handling before slaughter. Records provided by a processing plant for loads transported over a 19 mo period during 2009 and 2010 were examined. The median percentage of wing injuries per load was 5.7%, whereas injuries to the legs, breast, or shoulders were all less than 1% per load. Risk factors for wing injuries were examined by considering the data from each load by handling event (i.e., loads originating from the same producer on the same date). A multilevel model with three levels, producer (n = 86), handling event (n = 1694), and load (n = 4219), was fitted. The final model included weight, sex, season, catching team, time of day at which loading began, speed of loading, and an interaction between speed of loading and time of day. Factors that reduced the risk of wing injuries were loading lighter birds, loads containing only cockerels, and loading in the fall. The predicted percentage of wing injuries was relatively constant for slower loading speeds, but it was increased significantly when faster loading speeds were adopted during daytime (0700–1700). Identification of these risk factors can be used to adjust loading practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".