Automated methods for detecting lameness and measuring analgesia in dairy cattle
Why this work is in the frame
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Bibliographic record
Abstract
The objective was to assess gait, automated measures of weight distribution among the legs, and daily activity as methods for detecting lameness in dairy cows and measuring pain mitigation by nonsteroidal antiinflammatory drugs. Fifty-seven lactating cows (28 of which were lame) were injected twice with ketoprofen (3.0 mg/kg i.m.) or isotonic saline solution. Gait scores (numerical rating system, NRS), time spent lying down, frequency of steps, and weight distribution among legs when standing before, during, and after injections were measured to assess whether automated measures of activity can detect lameness and the effect of analgesic drugs in cows. Lame cows (NRS >3) shifted weight between contralateral legs more often (SD of the weight applied: 31.1+/-2.1 vs. 24.5+/-1.9kg), had a greater asymmetry in the weight applied to the rear legs (leg weight ratio=0.78+/-0.02 vs. 0.87+/-0.02), had longer lying bouts (94.0+/-4.9 vs. 78.2+/-5.8min), and walked slower (1.28+/-0.3 vs. 1.42+/-0.3 m/s) than nonlame cows. Variability over time (SD) of the weight applied to the rear legs was the most accurate predictor of whether a cow was lame or not (area under the curve=0.71). The SD of the weight applied to the rear legs decreased on the days when ketoprofen was given compared with the day before and after (18 and 12% decrease for lame and nonlame cows, respectively). Ketoprofen did not affect any other measure. Measures of weight shifting between legs while cows are standing have potential as an automated method of detecting lameness and analgesia.
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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 | 0.001 |
| 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