Risk factors affecting wing injuries of broiler chickens at a slaughter plant in New Brunswick, Canada
Bibliographic record
Abstract
An epidemiological study was conducted on risk factors affecting wing injuries of broiler chickens during catching and transportation to a slaughter plant in New Brunswick, Canada. The slaughter plant provided detailed information about the truck loads of chickens transported to the plant between January 2009 and July 2010. All of the information was collated into a single file. The data was divided into different handling events, with which each event representing a collection of loads coming from the same producer during a single time period (handling event). A multilevel model with three levels: producer (86), handling event (1694) and loads (4494) were fitted to the data. The final model included seven variables: weight, sex, season, catching team, time of day during catching, speed of catching and the interaction between speed of catching and time of day during catching. An increase in bird weight lead to an increase in the occurrence wing injuries (P< 0.001). The model shows that loads with mixed sex and pullets had higher percentage of wing injuries than loads with cockerels (P<0.001). Loading in the fall resulted in significantly decreased wing injuries compared to loading in the winter, spring and summer (P<0.001). There was significant difference in percentage of wing injuries between different catching teams (P<0.001). The effect of time of day was dependent on the speed of catching. However the percentage of injuries is always lower in the night time regardless of the speed of catching. In the afternoon the percentage of injuries were higher especially if the speed of catching was higher.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".