Bibliographic record
Abstract
We compared the prevalence and severity of skin lesions on the hocks of lactating dairy cows in southern British Columbia, comparing 20 farms using three common bedding surfaces: sawdust, sand, and geotextile mattresses. Skin lesions were scored at five positions on the hock. For each position we noted if the lesion showed inflammatory attributes, and then assigned a severity score. Of the 1752 lactating cows scored, 1267 cows (73%) had at least one hock lesion. Of those cows with lesions, 87% had lesions on both legs, 76% had lesions on more than one location on the hock, and 78% had a lesion of at least moderate severity (i.e., evidence of skin breakage or an area of hair loss >10 cm2). Lesions were most prevalent on farms that used geotextile mattresses (91% of cows) and least common on farms that used sand (24% of cows). Moreover, lesions on cows from farms using mattresses were more numerous and more severe than those on cows from sand-bedded farms. The prevalence and severity of lesions on farms using sawdust was intermediate. Lesions also varied in relation to location on the hock. For farms using geotextile mattresses, lesions were more common and more severe on the lateral surfaces of both the tuber calcis and the tarsal joint. On farms using sawdust, lesions were common on the dorsal surface of the tuber calcis and the lateral surfaces of both the tuber calcis and the tarsal joint. Lesions were rare on all five positions for cows from sand-bedded farms. Among the 10 farms sampled using sawdust, we found a significant negative relationship between the length of the stall and severity of lesions. For cows with lesions, the number and severity of lesions increased with age.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".