Using in-line milk progesterone data to characterize parameters of luteal activity and their association with fertility in Holstein cows
Bibliographic record
Abstract
Our objectives were to characterize parameters of luteal activity based on milk progesterone concentration (P4c) data from before and after artificial insemination (AI) and to evaluate their potential association with fertility in Holstein cows. Records of AI events (n = 4,353) and of milk P4c (n = 158,961) obtained through an in-line milk analysis system (Herd Navigator, DeLaval International, Tumba, Sweden) from 1,891 lactations of 1,423 Holstein cows were evaluated. Milk P4c (ng/mL) were measured every 2.2 ± 1.9 d (mean ± standard deviation) between 23.6 ± 7.3 and 185.3 ± 56.7 d in milk. Variations in milk P4c of consecutive records were used to determine onset of luteal phase (increase in P4c from <5.0 to ≥5.0 ng/mL), luteal phase length (period, in days, of P4c ≥5.0 ng/mL), cessation of luteal phase (decline from ≥5.0 to <5.0 ng/mL, designated as P4c-decline), and pregnancy (AI followed by a luteal phase that remained uninterrupted until 50 d post-AI). The length of the luteal phase preceding AI, the highest P4c (P4c peak) during the luteal phase preceding AI, the lowest P4c preceding AI (P4c pre-AI) that followed a P4c-decline, and the interval between P4c-decline and AI were evaluated, as well as the interval between AI and onset of luteal phase, and P4c at early diestrus (4.5 ± 0.6 d post-AI), mid diestrus (10.0 ± 0.6 d post-AI), and late diestrus (14.1 ± 0.6 d post-AI). Data were analyzed using logistic regressions, and comparisons made based on quartiles and cut-points established by receiver operating characteristic curve analysis. Overall probability of pregnancy was 32.0%. Parameters associated with reduced probability of pregnancy (represented as percentage points decrease in the probability of pregnancy) were (1) luteal phase length >14.4 d (7.6% decrease), (2) P4c peak ≤24.7 ng/mL (4.5% decrease), (3) P4c pre-AI >0.5 ng/mL (5.5% decrease), (4) interval between P4c-decline and AI of >1.6 d (4.0% decrease), (5) interval between AI and onset of luteal phase of <7 or >11 d (9.3 and 12.1% decrease, respectively), and (6) P4c at early diestrus ≤0.7 or >3.5 ng/mL (15.2 and 6.7% decrease, respectively), (7) P4c at mid diestrus ≤12.4 ng/mL (12.5% decrease), and (8) P4c at late diestrus ≤22.7 ng/mL (9.7% decrease). The parameters of luteal activity associated with reduced probability of pregnancy established here could be used as benchmarks while developing recommendations to improve reproductive performance in herds using in-line milk progesterone monitoring.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".