Effect of Hoof Pathologies on Subjective Assessments of Dairy Cow Gait
Bibliographic record
Abstract
To explore how hoof pathologies affect dairy cattle gait, we studied cows with sole hemorrhages (n = 14), sole ulcers (n = 7), and those with no visible injuries (n = 17). Overall gait assessments, scored from video using a 1 to 5 numerical rating system (1 = sound, 5 = severely lame) and a continuous 100-unit visual analog scale, found cows having sole ulcers had poorer gait than healthy cows (mean +/- SEM: 4.0 +/- 0.13 vs. 3.1 +/- 0.08, and 59 +/- 3 vs. 46 +/- 2, respectively). Six gait attributes (back arch, head bob, tracking-up, joint flexion, asymmetric gait, and reluctance to bear weight) were also assessed using continuous 100-unit scales. Compared with healthy cows, those having sole ulcers walked with a more pronounced back arch (12 +/- 3 vs. 28 +/- 4), more jerky head movement (2 +/- 2 vs. 10 +/- 3), shortened strides (7 +/- 2 vs. 26 +/- 4), and more uneven weighting among the limbs (16 +/- 2 vs. 32 +/- 3). Of all measures, the numerical rating system most effectively discriminated healthy cows from those with sole ulcers (R2 = 0.73), classifying 92% of animals correctly. No differences were detected among cows with and without sole hemorrhages. Intra- and interobserver reliabilities were reasonable for all measures (R2 > or = 0.64) except joint flexion and asymmetric gait. In summary, subjective assessments of dairy cattle gait provide valid and reliable approaches to identifying cattle with sole ulcers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".