Cow- and farm-level risk factors for lameness on dairy farms with automated milking systems
Bibliographic record
Abstract
Lameness is a major concern to animal health and welfare within the dairy industry. Our objectives were to describe the prevalence of lameness in high-producing cows on farms with automated milking systems (AMS) and to identify the main risk factors for lameness at the animal and farm level. We visited 36 AMS farms across Canada and Michigan. Farm-level factors related to stall design, bedding use, flooring, and stocking rates were recorded by trained observers. Cows were scored for lameness, leg injuries, body condition (BCS), and body size (hip width and rump height; n=1,378; 25-40 cows/farm). Mean herd prevalence of clinical lameness was 15% (range=2.5-46%). Stall width relative to cow size and parity was found to be the most important factor associated with lameness. Not fitting the average stall width increased the odds of being lame 3.7 times in primiparous cows. A narrow feed alley [<430cm; odds ratio (OR)=1.9], obstructed lunge space (OR=1.7), a low BCS (OR=2.1 for BCS ≤2.25 compared with BCS 2.75-3.0), and presence of hock lesions (OR=1.6) were also identified as important risk factors for lameness. Only 1 of 36 farms had stalls of adequate width and length for the cows on their farm. For lameness prevention, it can be concluded that more emphasis needs be placed on either building stalls of appropriate width or selecting for smaller-framed cows that fit the existing stalls.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".