Feeding Behavior Identifies Cows at Risk for Metritis
Bibliographic record
Abstract
Identification of sick animals is a key component of any dairy herd health program. Metritis, one common disease following calving, can be a costly disease to producers. These costs are incurred by increased days open, lower first-service conception, more inseminations, and failure to become pregnant, leading to involuntary culling. Clearly, an improved ability to identify or predict metritis will help avoid these costs by aiding prevention and early treatment. Previous research has indicated that cows with lower feed intakes are more likely to be diagnosed with metabolic and infectious diseases during the transition period. However, changes in feed intake must ultimately result from changes in feeding behavior. Moreover, feeding behavior has been shown to predict morbidity in feedlot steers and may be similarly useful for prediction of disease in transition dairy cows. There is little opportunity to monitor individual feed intake on commercial farms due to prohibitive costs; however, electronic monitoring of feeding behavior shows greater promise for commercial application. This paper will present and discuss studies conducted by our research group that provide evidence that changes in prepartum feeding behavior can be used to identify cows at risk of postpartum metritis.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".