Spatial distribution of cannibalism mortalities in commercial laying hens
Bibliographic record
Abstract
The distribution of cannibalism cases in a flock of 19,776 Babcock White Leghorns was monitored from 21 to 54 wk of age. The hens were kept in a single-floor house consisting of four banks of two-deck stair-step cages. Each of the 4,944 cages held four hens at a density of 152 cm(2) (60 inches(2)) per hen. Each cage was assigned a number from 1 to 4,944, and each dead bird was tagged according to its cage of origin. Dead birds were collected daily, kept in a freezer, and necropsied weekly. Farm personnel routinely transferred a live hen from an end cage to a cage where a mortality had occurred. The cause of death, age, cage number, and cage location were recorded for each dead hen. Of the 1,173 hens that died during the study period, 253 (21.6%) died from egg peritonitis, 184 (15.7%) from hypocalcemia, 167 (14.1%) from cannibalism, 164 (14%) from neoplastic disease, and the rest from various other causes. Cannibalism cases were analyzed statistically for clustering. Cannibalism was defined as death from tissue trauma and hemorrhage inflicted by cage mates. A spatial analysis showed that cannibalism is not a random event but one that occurs in clusters. The incidence of cannibalism was also found to be significantly higher on the top rows of cages as compared with the bottom rows.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".