Technical note: A comparison of 2 methods of assessing lameness prevalence in tiestall herds
Bibliographic record
Abstract
We compared 2 methods for identifying lame cows and estimating the prevalence of lameness in tiestalls. Cows (n=320) in 9 tiestall herds were scored as lame both by the presence of limping while walking and by stall lameness scores (SLS). The SLS was based on the number of the following behaviors that the cow showed while standing in the tiestall: weight shifting, standing on the edge of the stall, uneven weight bearing while standing, and uneven weight bearing while moving from side to side. Two observers watched video-recordings of the cows. Intraobserver agreements for the 4 SLS behaviors ranged from 92 to 100%, and interobserver agreement ranged from 81 to 100%. The overall prevalence of lameness based on an SLS of ≥2 was similar to that of limping (39 vs. 40%). The sensitivity of the classification based on the SLS was 0.63 and the specificity was 0.77 in identifying cows with a limp; accuracy varied across farms from 62.2 to 80.4%, with a mean of 71.7%. A cow with an SLS of ≥2 had 4.88 times the odds of limping than a cow with an SLS of <2. The prevalence of lameness on farms based on SLS was highly correlated with the prevalence of limping (Pearson correlation=0.88; n=9), and prevalence estimates from the 2 methods diverged most when the mean herd prevalence was lower. The SLS method provides an estimate of the prevalence of lameness in tiestall herds comparable with traditional gait scoring, but does not require that the cows be untied. The SLS method could be used to improve lameness detection on tiestall farms and obtain estimates of lameness prevalence without the need to walk the cows.
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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 | 0.125 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".