Association Between a Visual and an Automated Locomotion Score in Lactating Holstein Cows
Bibliographic record
Abstract
Two studies were conducted to evaluate visual locomotion scoring (VLS) and Stepmetrix locomotion scoring (SLS) in detecting painful digit lesions. In study 1, one veterinarian performed VLS. Cows with VLS > or = 3 were hoof trimmed and the presence or absence of a painful lesion (PL), defined as a reaction to digital pressure, was recorded. A strongly increasing pattern in the proportion of cows with PL was detected as VLS increased. The proportions of cows with painful lesions were 5.6% (n = 53), 20.1% (n = 78), 55.5% (n = 164), 79.9% (n = 159), and 100% (n = 5) for VLS 1 to 5, respectively. Study 2 was conducted on a different farm. The entire farm was visually locomotion scored by 3 veterinarians on the same day, and the cows were Stepmetrix locomotion scored by walking through the Stepmetrix system. Every cow was trimmed during the following 2 d by 1 of 8 professional hoof trimmers. The 3 veterinarians identified, scored, and recorded any PL. Interobserver agreement for the 3 veterinarians had a kappa coefficient of between 0.45 and 0.48 +/- 0.05. In total, 518 cows were used in the analysis, from which 11.2% were identified with a PL. Of the cows diagnosed with a PL, 32.8% were detected with a sole ulcer, 25.9% with white line disease, 13.8% with white line abscess, and 27.5% with other diseases. A receiver operating characteristic analysis was performed; the area under the curve was larger for VLS (0.80; 95% confidence interval, 0.76 to 0.83) than SLS (0.62; 95% confidence interval, 0.57 to 0.66). When performed by trained veterinarians, VLS performed better than SLS in detecting PL.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".