Can 60 days of feeding lead to increased fitness for transport in cull dairy cows?
Bibliographic record
Abstract
The welfare of cull cows during transport to slaughter is a current concern in the Canadian dairy industry. Cull cows sold through auction often have a high prevalence of lameness, low BCS, hock lesions, and udder engorgement. To evaluate whether drying off and feeding cull dairy cows before transport can mitigate these challenges, 45 cows designated for culling were randomly assigned to either be fed for 60 d after being dried off (fed group; n = 24) or to serve as controls by being sent directly to slaughter (direct group; n = 21). Two fed group cows were removed for health reasons before completing the feeding period. Both fed group and direct group cows were assessed for locomotion (5-point scale), BCS (5-point scale), hock lesions (3-point scale), udder engorgement (3-point scale), and BW at the time of enrollment. Fed group cows, locomotion, BCS, hock, and udder engorgement scores were assessed weekly until slaughter. Weights of the fed group cows were measured again the day before slaughter. Mixed linear regression models were used to assess continuous outcomes BCS and weight. Mixed logistic regression models were used to assess dichotomous outcomes presence of hock lesions and lameness. Fed group cows gained an average of 116.9 kg over the feeding period (SE ± 8.20). Fed group cows had an average weight at slaughter of 834.2 kg, whereas direct group cows' average weight was 767.3 kg (SE ± 26.8). The fed group cows' average BCS at the start of the trial was 2.4, and at slaughter was 3.6, with an average gain of 1.2 BCS points. At slaughter, the proportion of udders involuted in the fed group was 45.1% (10/22) and in the direct group cows, was 0% (0/21). No differences were found in locomotion or hock lesions between the fed group and direct groups. It is important to weigh potential benefits for the fed group cows with the fact that direct group cows did not endure a drying off procedure, nor were they placed at risk of potential adverse health events. However, despite these potential limitations, due to the improved BCS and udder engorgement scores, cows fed for 60 d may be better prepared for the transportation to slaughter, as well as sell for a higher price due to increased BW and body condition.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".