Tail chalk improved detection of true estrus alerts from an automated activity monitor system in a cohort study
Bibliographic record
Abstract
This study aimed to assess how two estrus detection tools, an automated activity monitor (AAM) and tail chalk (TC), were associated with true estrus events in lactating Holstein cows. Cows with days in milk ≥5 were monitored twice daily for estrus alerts, identified by the AAM and a TC score of 3 (<50 % of chalk remaining). Transrectal ultrasonography was performed on the day of alert to confirm estrus (cows with a follicle ≥10 mm and corpus luteum ≤24 mm). For AAM-identified events, peak activity and duration of estrus were measured. Mixed logistic and linear regression models were used to assess associations for dichotomous and continuous outcomes, respectively. A total of 1714 events were recorded, of which 1098 from 371 cows were considered in true estrus (AAM only: n = 629; TC only: n = 86; AAM + TC: n = 383). Cows with both AAM and TC alerts had higher odds of true estrus compared to those with only AAM alerts (OR = 4.24; 95 %CI = 3.05-5.87) or only TC alerts (OR = 17.70; 95 %CI = 11.90-26.34). Cows having AAM and TC alert had greater peak activity index (AAM only = 221.64 ± 7.07; AAM + TC = 323.89 ± 9.13) and longer duration of estrus (AAM only = 8.37 ± 0.27 h; AAM + TC = 13.17 ± 0.33 h) compared to cows with AAM alerts only. In conclusion, TC could be used as a marker of high-intensity estrus and, when combined with an AAM, improves identification of true estrus in dairy cows.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".