359 Transportation of cull dairy cows in British Columbia: duration and effects on cow condition.
Bibliographic record
Abstract
Cull dairy cows are regularly removed from dairy herds and enter the marketing system which involves transportation to public auction markets and then to an abattoir. The objective of this study was to follow cull cows from farm to abattoir and to monitor changes in the cows’ condition. From May 2017 to March 2018, data were collected from 20 dairy farms and 3 abattoirs in British Columbia, 1 abattoir in Alberta, and 2 abattoirs in USA. The dairy farms were visited regularly before cows were removed from the herds and a researcher scored the animals for body condition (BCS; 5-point scale), lameness (5-point scale) and udder condition (3-point scale). Trained assessors at the abattoirs also assessed cows’ condition at arrival. Mixed effects and glimmix models were used to test the effect of transport on the animals’ condition, with cow and farm of origin assigned as random effects. During the study, 1,220 cull cows were removed from participating farms and 469 of those cows were assessed at one of the participating abattoirs. After leaving the farms, cows spent 79.6 ± 1.9 hours in the marketing system until being processed. Including delays at auctions or assembly yards, about 43% of cows were in transit for 4–6 days and 4% for 7–9 days. Regarding distance, around 16% of cull cows were transported 1,100 km from farm to abattoir. Cull cows reduced their BCS from 3.1 ± 0.02 at the farm of origin to 2.6 ± 0.03 at the abattoir (P<0.0001). Lameness did not change, but transport increased the development of acute milk accumulation and udder inflammation from 8% at farm of origin to 41% at the abattoir (P<0.0001). This information about delays to slaughter and changes in cow condition could help producers make better culling decisions.
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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 | 0.003 |
| 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) | 0.003 | 0.001 |
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".