Short communication: Herd-level reproductive performance and its relationship with lameness and leg injuries in freestall dairy herds in the northeastern United States
Bibliographic record
Abstract
The objectives of this study were to describe herd-level reproductive outcomes and their associations with the prevalence of lameness, hock injuries and knee injuries in freestall dairy herds in the northeastern United States. Five reproductive outcomes (calving to conception interval, CCI; calving interval, CI; conception risk at the first artificial insemination, CR1; insemination rate, IR; and pregnancy rate, PR) were measured from Dairy Comp 305 (Valley Agricultural Software, Tulare, CA) for a 12-mo period for all multiparous cows in each of the 53 herds assessed. The prevalence of lameness, hock injuries, and knee injuries was assessed in 1 high-producing group. The means (± standard deviation) for the 5 reproductive outcomes were as follows: CCI = 128 ± 10 d, CI = 404 ± 10 d, CR1 = 36 ± 5%, IR = 60 ± 7%, and PR = 20 ± 3%. The average prevalence of clinical lameness, hock injuries, and knee injuries were 45 ± 20%, 58 ± 31%, and 16 ± 15%, respectively. Univariable associations between the reproductive outcomes and the prevalence of lameness and leg injuries were tested and significant predictors were submitted to a model that controlled for the confounding effects of herd size, 305-d mature equivalent milk production of the high-producing group, and use of deep bedding. A higher prevalence of lameness was associated with poorer reproductive performance, although the relationships were weak: herds with a higher prevalence of lameness had longer average CCI (slope estimate = 0.16 ± 0.07; R(2)= 0.09) and CI (slope estimate = 0.14 ± 0.07; R(2) = 0.07). These results indicate that management to reduce lameness may improve reproductive performance.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".