217 Lameness, foot lesions and injuries: impacts on the cow, farmer and consumer.
Bibliographic record
Abstract
Lameness in dairy cattle is a clinical sign of pain related to the locomotor system, primarily caused by foot lesions. It is production-limiting and the dairy industry’s most visible animal welfare concern. Canadian producers rated foot and leg problems as the 3rd most common reason for involuntary culling and in a broad survey of industry stakeholders, lameness was ranked as the most important health issue. Lameness and other animal-based measures (body condition, body injuries) provide information on cows’ response to their environment and management. Due to concerns about the welfare, health and financial costs related to lameness, several Canadian initiatives in the areas of research, industry and extension have been aimed at reducing its occurrence. For research, the focus has been on identifying lameness prevalence and associated risk factors, as this is essential for disease prevention and control. Our research indicated that across Canada, 22% of cows were lame while within-herd prevalence ranged from 0–69%. However, lameness prevalence estimated by producers averaged 9%, which highlights the challenges with detection. Lameness was higher on farms with poor comfort of surfaces for standing and lying. Digital dermatitis (an infectious, painful skin disorder) was the primary foot lesion, affecting 22% of cows and 94% of herds. Suboptimal footbath management and hygiene contributed to the high digital dermatitis prevalence. Dairy Farmers of Canada recently launched a mandatory on-farm animal care assessment (proAction Animal Care), where every farm is subject to evaluation of injuries, body condition and lameness by a third-party. It is anticipated that a significant number of producers will not meet prescribed standards, in particular the maximum acceptable proportion of lame cows. For extension, projects such as the Alberta Lameness Reduction Initiative have developed tools and information that allow producers and their advisors to develop lameness mitigation plans specific to their individual farms.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".