An audit of transport conditions and arrival status of slaughter cattle shipped by road at an Ontario processor
Bibliographic record
Abstract
This is an observational study to investigate slaughter cattle transportation conditions in Canada. Data collected include: length of time in transit; temperature variation; season; weather transport conditions; cattle weight; sex and whether sexes were separated on mixed loads; number of lots and whether lots were separated; cattle unloading speed; cattle handling score; trucker training and experience hauling cattle; ventilation; and condition of cattle at arrival. Information was collected on approximately 50 000 animals transported by 1363 trucks. All but 0.2% of trucks arrived within the 52 h allowable transport time before unloading required for rest, feed, and water. Most trucks (85.7%) were from within 8 h of the plant. Trucks surveyed were at or above the recommended space allowance 49% of the time. There were five non-ambulatory (unable to walk off the truck with or without assistance) or dead, 79 lame, and four animals that needed assistance of the 49 959 animals observed (0.4, 4.8 and 0.2%, respectively, of the trucks surveyed). However, these concerns were not necessarily a result of transportation, as animal health at loading was unknown. There were very few visible animal welfare concerns associated with the transportation of slaughter cattle in the population sampled. Key words: Cattle, transport, welfare, 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".