1258 Estrus detection intensity and accuracy, and optimal timing of insemination with automated activity monitors for dairy cows
Bibliographic record
Abstract
The objectives were to assess: the ability of automated activity monitoring (AAM) to detect estrus for first insemination; the accuracy of detection; and the optimum interval from the onset of estrus to insemination. Four commercial farms were studied over 1 yr; 2 employed the AfiAct (Afikim) system and 2 the Heatime HR (SCR Inc.) system. Cows were inseminated between 55 and 80 DIM based on AAM only, then supplemented with timed AI (TAI). Blood progesterone was measured in 1,014 cows at weeks 5, 7, and 9 postpartum; purulent vaginal discharge (PVD) was assessed at week 5 and lameness and BCS at week 7. Overall, AAM detected 83% of cows in estrus by 80 DIM. Cows that had 3 serum P4 < 1 ng/mL, had PVD, or were both lame and had BCS ≤ 2.5 were less likely to be detected in estrus by 80 DIM (62, 68, 53%, respectively). Blood samples were collected on the day of 445 AI based on AAM and 323 TAI. The proportion of cows not in estrus (P4 > 1 ng/ml) on the day of AI was similar (P = 0.35) between AAM (4 ± 1.8%) and TAI (3 ± 1.2%). Activity data were extracted from AAM software for 1454 AI. Onset of estrus was calculated using the same (AfiAct) or similar (for the proprietary SCR algorithm) criteria as the AAM system. Producers recorded the time of AI. The interval from onset of estrus to AI was categorized as 0 to 8, 8 to 16, or > 16 h. There was no effect of AAM system on the probability of pregnancy per AI, but there was an interaction of interval with parity. For multiparous cows, the probability of pregnancy per AI was 31%, which did not differ (P = 0.7) with the interval to AI. For primiparous cows, the odds of pregnancy were greater if AI occurred 0 to 8 h (49%) than 8 to 16 h (36%) or > 16 h (31%) after the onset of estrus. AAM can detect estrus for first AI in just over the length of 1 estrus cycle for over 80% of cows, but the remainder would likely require intervention. For multiparous cows, performing AI based on AAM once per day would not affect pregnancy per AI, but for primiparous cows AI within 8 h of the onset of estrus may be advantageous.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".