Using automated activity monitoring to detect resumption of cyclicity in early lactation—Meta-analysis
Bibliographic record
Abstract
This meta-analysis aimed to evaluate the efficacy of automated activity monitoring (AAM) in detecting estrous expression and ovulatory status in cows during the voluntary waiting period (VWP). A comprehensive literature search was conducted in PubMed, ScienceDirect, and Google Scholar using specific search terms. Inclusion criteria focused on studies that assessed estrous expression within the VWP using modern AAM systems alongside blood progesterone (P4) measurements. Four manuscripts involving 2,198 cows were included. Data extraction was performed by a single investigator and validated by a coauthor. The analysis considered estrous expression and ovulatory status determined through serial blood P4 measurements. Cows were classified based on P4 concentrations and estrus alerts into true positive, false positive, true negative, and false negative categories. Statistical analyses were conducted using MedCalc, incorporating 7 experimental groups from the selected manuscripts. The pooled proportion of ovulatory cows with P4 ≥1 ng/mL by 49 DIM (in most studies) was 79.8% (95% CI: 74.9%–84.3%), with significant heterogeneity (I 2 = 86.1%). The proportion of cows with estrous expression detected by AAM by 60 DIM (in most studies) was 64.0% (95% CI: 48.3%–78.3%), also showing significant heterogeneity (I 2 = 98.1%). Sensitivity and specificity of AAM systems to identify ovulatory cows were 70.3% (95% CI: 55.1%–83.4%) and 60.0% (95% CI: 42.5%–76.3%), respectively, both with significant heterogeneity. Positive predictive value was 88.1% (95% CI: 84.9%–91.0%), and negative predictive value was 35.3% (95% CI: 26.3%–44.7%), indicating variability among experimental groups. The findings suggest that although AAM systems show promise in confirming ovulation in early lactation, the lack of estrus detection by AAM does not confirm anovulatory status. The heterogeneity in the data suggests there might be inconsistencies in the precision or configuration of the AAM systems across farms and studies.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.060 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".