PSXIII-40 Monitoring in-line milk progesterone profiles prior to first breeding to predict reproductive performance in Holstein cows.
Bibliographic record
Abstract
We investigated if milk progesterone (P4) profiles determined by an in-line milk P4 analysis system (Herd NavigatorTM, DeLaval Inc.) could be used to predict subsequent reproductive performance. Specifically, we evaluated associations of (1) commencement of luteal activity (C-LA), (2) first luteal phase length, (3) first P4 peak, and (4) number of luteal phases prior to first breeding, with pregnancy at first AI (P/1stAI) and cumulative pregnancy by 150 (P/150) d in milk (DIM). Milk P4 concentrations (ng/mL) were measured approximately every 2 d from ~21 DIM until pregnancy in 1,354 lactations (1,190 cows). Variations in P4 were used to define luteal function, such as C-LA, first luteal phase length, and pregnancy. All AI occurred within 5 d of P4 declining below 5 ng/mL. Variables were categorized into quartiles and data analyzed using GLIMMIX and PHREG procedures of SAS. First AI occurred at 70 ± 17 DIM. Overall P/1stAI and P/150 were 25.6 and 62.3%, respectively. Parameters associated with decreased probability of P/1stAI were first luteal phase length ≥ 17 vs. < 17 d (Odds Ratio [OR]: 0.13; P < 0.01), first P4 peak ≤ 17 vs. > 17 ng/mL (OR: 0.13; P < 0.01), and having one vs. two luteal phases prior to first AI (OR: 0.53; P < 0.01). The likelihood of P/150 was increased in cows that had C-LA before 50 than after 50 DIM (Hazard ratio: 0.64; P < 0.01), and in cows that had a first luteal phase ≥ 17 vs. < 17 d long (Hazard ratio: 0.58; P < 0.01). In summary, prolonged first luteal phase (≥ 17 d), low first P4 peak (≤ 17 ng/mL) and having only one luteal phase prior to first breeding reduced P/1stAI. Furthermore, a delayed C-LA (> 50 DIM) and a prolonged first luteal phase reduced likelihood of P/150.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".