Association Between a Visual and an Automated Locomotion Score in Lactating Holstein Cows
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it