Road transport conditions of slaughter cattle: Effects on the prevalence of dark, firm and dry beef
Bibliographic record
Abstract
TThis is a benchmark study to investigate slaughter cattle transportation conditions in Canada. Data collected included: season; temperature variation; truck ventilation; transport conditions; length of time in transit; trucker training and experience hauling cattle; number of lots and whether lots were separated; sex and whether sexes were separated on mixed loads; cattle unloading gait score; cattle handling score; cattle weight and number of dark cutters. Information was collected on approximately 50 000 animals transported by 1363 trucks. The prevalence of dark cutters (mean = 2% per truckload) was highest in mixed loads, followed by heifers and steers. Mixed loads that were not separated (steers and heifers in the same compartment) had a greater prevalence of dark cutters than mixed loads that were separated. The GLIMMIX procedure in SAS 9.1 was used for the analysis of the risk factors associated with dark cutters. Province of origin, cattle unloading speed, driver training, truck ventilation, trucking experience, sex, origin (sale barn or feedlot) and whether or not cattle were held in lairage overnight were all significant predictors for dark cutting beef.
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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 | 0.001 | 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) | 0.002 | 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".