Tie-Stall Design and its Relationship to Lameness, Injury, and Cleanliness on 317 Ontario Dairy Farms
Bibliographic record
Abstract
The objective of this study was to identify relationships between tie-stall design and selected cow-based injury, lameness, and cleanliness measurements. All lactating dairy cows (n = 17,893) from 317 Ontario tie-stall dairy farms were evaluated once between March and September 2003. Stall dimensions were recorded and cows were scored for neck and hock lesions, broken tails, back arch, hind claw rotation, and udder and hind limb cleanliness. Neck lesions were significantly associated with tie-rail height. Hock lesions were positively associated with presence of an electric trainer and hind limb and udder cleanliness and negatively associated with tie-chain length. A negative association was found between broken tails and tie-rail height and a positive association between broken tails and udder and hind limb cleanliness. As mean stall length increased, fewer cows had hind-claw rotation. Having more dirty cows was associated negatively with stall length and chain length, and positively associated with the presence of an electric trainer. Proportion of cows with clean udders increased with the percentage of cows with clean hind limbs and with tie-rail height. As the prevalence of clean udders increased the prevalence of broken tails decreased. This study provides insight into how tie-stall dimensions may influence aspects of dairy cow welfare. Application of these principles could improve tie-stall design.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".