The use of calving behaviours and automated activity monitors to predict and detect parturition and uterine diseases in Holstein cattle
Bibliographic record
Abstract
Proactive management practices during the transition period are necessary to reduce the risk and mitigate the effect of transition diseases, such as dystocia and uterine diseases, in dairy cows. The aims of this thesis were to 1) investigate the relationship between the duration of labour, calving assistance, and uterine diseases (metritis and subclinical endometritis), and 2) investigate the test performance of relative changes in activity and lying behaviour using two automated activity monitors (AAM) to predict calving time and uterine disease. Holstein cows (n = 567) were followed from 3wk before to 3wk after calving. Lying behaviour and activity were monitored continuously by the AAMs. Cameras were used to record calving time and duration and calving assistance was recorded. Metritis was diagnosed based on vaginal discharge and body temperature measured at 6 and 12DIM, while subclinical endometritis was based on cytological examination at 42 ± 3DIM. Duration of labour was estimated as time from the appearance of the amniotic sac until the calf was expelled. Within study 1, we determined that there was a quadratic relationship between metritis and duration of labour for assisted cows, where the probability of metritis was greatest at the shortest and longest durations of labour, but the lowest probability of metritis (28.2%) was at approximately 130 min. Probability of metritis in unassisted cows was not associated with duration of labour. Subclinical endometritis was not associated with the duration of labour or calving score. For study 2, a relative decrease of 44% in lying time per bout, 8 h before calving, resulted in the best performance (AUC = 0.76; Se = 67%; Sp = 77%). Distinct changes in activity and lying behaviour were observed prior to calving and within cows diagnosed with uterine disease. Although current technologies show promising results for on-farm detection of calving, they may not be a reliable method for detection of uterine disease. Future research should focus on refining the efficacy of AAMs for the use of health detection.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".