Lying behavior as an indicator of lameness in dairy cows
Bibliographic record
Abstract
Lameness is widely recognized as one of the most serious welfare and production concerns in the dairy industry. Our objectives were to evaluate the associations between lying behavior and lameness, and to determine whether lying behavior can be used as a diagnostic tool for lameness. Electronic data loggers recorded lying behavior of 1,319 cows from 28 farms at 1-min intervals for 5 d. These cows were gait scored according to a 5-point Numerical Rating System (NRS), and categorized as NRS or=3) and SEVLAME (NRS=4). Data were divided into 2 groups: 11 farms using deep-bedded stalls (DB) and 17 farms using mattress stalls (MAT). Differences in the daily lying time (h/d), frequency of lying bouts (n/d), duration of lying bouts (min/bout), and the standard deviation of bout duration (min/bout) between LAME or SEVLAME cows and those that were not were tested using mixed models. Receiver operating characteristic curves were constructed to identify behavioral thresholds to distinguish SEVLAME cows from the rest. Odds ratios for SEVLAME were estimated using logistic regression. Overall, 28.5% of cows were LAME including 7.3% that were SEVLAME. The prevalence of SEVLAME was higher on MAT farms than on DB farms (9.3+/-1.3 vs. 4.4+/-1.2%, respectively). SEVLAME cows on DB farms spent 12.8 [confidence interval (CI): 12.0 to 13.7] h/d lying down compared with 11.2 (CI: 10.7 to 11.8) h/d for cows that were not SEVLAME. These cows had longer duration of lying bouts [95.3 (CI: 84.6 to 107.3) vs. 80.3 (CI: 74.9 to 86.1) min/bout] and greater SD of bout duration [44.4 (CI: 41.1 to 48.0) vs. 50.7 (CI: 44.1 to 58.3) min/bout]. There were no behavioral differences among lameness categories on MAT farms. Within DB farms, cows with lying times >14.5 h/d had 16.2 (5.8 to 45.2) times higher odds of being SEVLAME. Cows with average lying bouts >90 min/bout were at 3.0 (1.2 to 7.4) times higher odds of being SEVLAME, and cows with average SD of bout duration >55 min/bout were at 4.1 (1.7 to 9.9) times higher odds of being SEVLAME. These results show that high lying times, long lying bouts, and variability in the duration of lying bouts were associated with lameness, and that stall surface influenced the behavioral responses of lame cows.
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.
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 | 0.003 |
| 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 | 0.000 | 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".