Feeding Behavior Identifies Dairy Cows at Risk for Metritis
Bibliographic record
Abstract
Dairy cows experience a high incidence of disease and metabolic disorders in the weeks immediately following calving, but early and accurate diagnosis remains a challenge. Cows suffering from metritis, one common disease following calving, exhibit reduced milk yield and reproductive performance. However, afflicted cows show few overt signs of illness and frequently go unnoticed in the absence of veterinary examination. To determine whether changes in feeding behavior could be used to identify animals at risk for metritis, attendance at the feed alley was monitored continuously for 26 Holstein cows during the transition period, beginning 2 wk before and ending 3 wk after calving. Every 3 +/- 1 d, cows were examined for metritis based on rectal body temperature and condition of vaginal discharge. Over the 3 wk of observations after calving, 69% of cows showed some signs of metritis. These cows spent on average 22 min/d less time at the feed alley during the transition period than did non-metritic cows. For every 10-min decrease in average daily feeding time, cows were twice as likely to be diagnosed with metritis. A threshold of 75 min of average daily feeding time was 89% sensitive and 62% specific for detection of acute metritis. In conclusion, reduced time at the feeder can be used to identify dairy cows at risk for metritis. More research is required to determine how soon before calving at-risk cows can be identified and whether these behavioral differences can also be used in the early diagnosis of other diseases or metabolic disorders.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".