Deviations in behavior and productivity data before diagnosis of health disorders in cows milked with an automated system
Bibliographic record
Abstract
To explore potential changes in behavior and productivity useful for early detection of health disorders in cows milked with automated milking systems (AMS), we collected longitudinal data throughout lactation of 57 dairy cows housed in a freestall barn with an AMS. Health problems were recorded, including subclinical ketosis (SCK; n = 19), metritis (n = 11), hoof disorders (n = 14), pneumonia (n = 7), and displaced abomasum (DA; n = 5). Data on rumination, activity, milking frequency and yield, and lying behavior were recorded electronically. Using repeated-measures mixed linear regression models, these data were analyzed for the days before the day of diagnosis/treatment (d 0) for each disorder separately, controlling for days in milk and parity. Analyses were performed between the day on which each outcome variable deviated significantly from baseline (up to d -14) and the day before diagnosis (nadir at d -1, before treatment and recovery). Outcomes tested were 3-d rolling averages of milk yield, milking frequency, and AMS supplement intake, in addition to daily rumination time (DRT), body weight, milk temperature, activity (measure of head/neck motion), and 3 lying behavior variables. From d -8, -6, and -5 before diagnosis of DA, SCK, or pneumonia, respectively, DRT declined by 45, 25, and 50 min/d. From d -14 to -1 before diagnosis of hoof disorders, DRT declined by 3 min/d. Body weight declined from d -4 before pneumonia (-14 kg/d) and metritis (-13 kg/d), from d -6 before SCK (-10 kg/d), and from d -5 before hoof disorders (-5 kg/d). Milk yield declined by 4.4 and 4.1 kg/d from d -4 before DA and pneumonia diagnoses, respectively, and by 1.2 kg/d from d -5 before SCK diagnosis. Activity levels declined before diagnosis of DA, pneumonia, SCK, or metritis. Lying behavior changed before diagnosis of DA, pneumonia, or metritis. Our results provide evidence that rumination behavior often deviated before milk yield and that several variables could contribute to earlier or automated identification of disorders. Behavior and productivity changed differently in association with various health disorders, suggesting the potential to distinguish among health problems. These variables merit further investigation in larger studies of cows milked with AMS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".