In-line milk progesterone monitoring as a tool for precision reproductive management
Bibliographic record
Abstract
In-line milk progesterone (IMP4) monitoring (Herd Navigator, DeLaval) is a technology that automatically detects onset of cyclicity, estrus, and pregnancy. Sampling starts at 20 DIM and occurs on average every 2 d until pregnancy. Estrus is detected based on a decline in progesterone (P4) concentrations below a threshold, and pregnancy is assessed from 30 to 55 d after AI in cows without return to estrus. Here, we review the potential of IMP4 as a tool for reproductive management. In a series of observational studies with up to 158,961 IMP4 records from 4,353 AI events, we characterized predictors of pregnancy per AI (P/AI) and investigated IMP4 profiles in cows returning to estrus. Some of the predictors included prolonged luteal phase before AI and suboptimal P4 levels at different time points before and after AI. Over one-third of cows had at least one characteristic of P4 profile unfavorable to P/AI, but with low predictive abilities. Among nonpregnant cows, 5% returned to estrus by 17 d after AI, 64% between 18 and 24 d, 16% between 25 and 30 d, and 15% between 31 and 55 d. This represents 85% of cows that are not pregnant 55 d after AI returning to estrus before 30 d, when first pregnancy diagnosis occurs in many dairies. Monitoring IMP4 might be used to identify subgroups of cows with different predicted P/AI to develop selective breeding strategies or targeted interventions. It can also aid in identifying nonpregnant cows early for timely reinsemination.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".