Behavioral changes to detect estrus using ear-sensor accelerometer compared to in-line milk progesterone in a commercial dairy herd
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Bibliographic record
Abstract
The first objective of this study was to compare behavioral and ear temperature changes using accelerometer ear tags (CowManager system; Sensor) during the declining progesterone (P 4 ) phase (expected estrus) and the luteal phase determined using in-line milk P 4 analysis (Herd Navigator system; HNS). The second objective was to evaluate the accuracy of each Sensor metric to detect estrus compared to HNS in a commercial dairy herd. Forty-six cows (23 young [1 to 2 lactations] and 23 mature [3 to 6 lactations]) at 20 days in milk (DIM) were fitted with Sensor tags, and P 4 profiles measured via HNS until 90 DIM. Sensor metrics analyzed were Resting, Ruminating, Eating, Active, High-Active, and ear temperature (Etemp). The day of milk P 4 decline below the 5 ng/mL threshold in the HNS was designated as d -1 (LSM ± SEM; 3.42 ± 0.08 ng/mL) and the day of expected estrus as d 0. Significant increases (LSM ± SEM) were observed at d 0 in Active (5.01 ± 0.14 min/h) and High-Active (8.70 ± 0.25 min/h) behavior responses as well as in Etemp (29.45 ± 0.08°C) compared with the luteal phase (Active: 4.46 ± 0.13 min/h; High-Active: 6.40 ± 0.22 min/h and Etemp: 28.69 ± 0.08°C). The greatest estrus detection accuracy (Youden Index [J: performance]) single metric was achieved using Etemp (0.24 J) followed by Resting (0.20 J) and High-Active (0.17 J) in all cows. Greater accuracy was observed in Young cows (Etemp: 0.44 J; Resting: 0.33 J; and High-Active: 0.25 J) than in Mature cows (Etemp: 0.09 J; Resting: 0.12 J; and High-Active: 0.13 J). Similarly, accuracy was greater when only healthy cows (cows with no postpartum health events) were compared (Etemp: 0.33 J; Resting: 0.31 J; High-Active: 0.20 J) to unhealthy cows (Etemp: 0.11 J; Resting: 0.02 J; High-Active: 0.02 J). The combination of behavior and Etemp metrics optimized the estrus detection accuracy in all the cows (0.30 J), Young (0.46 J), Mature (0.26 J), Healthy (0.45 J), and Unhealthy (0.11 J) cows compared to a single metric approach. Age and postpartum health affected the estrus detection accuracy using Sensor tags.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it