Technical note: Using an electronic drinker to monitor competition in dairy cows
Bibliographic record
Abstract
The objective of this study was to determine whether data from an electronic drinking system that tracks individual animal attendance with ear tag transponders could be used to detect social competition between dairy cows at the drinker. Specifically, we sought to identify the interval between one cow leaving the drinker and another cow taking her place that most accurately identified competitive replacements (when physical contact initiated by one cow causes the other to entirely remove her head from the drinker and the initiator subsequently places her head in the same drinker). The optimal interval to accurately identify replacements at the drinker was determined using a receiver operating characteristic curve and by evaluating the sensitivity (Se), specificity (Sp), and confusion matrix values (true positives, true negatives, false positives, false negatives) of different thresholds identified by 3 metrics: the Youden index, the point closest to (0,1) on the curve, and the point where Se roughly equals Sp. Lactating Holstein dairy cows (n = 20) were monitored for 4 consecutive 24-h periods by video recording and with an electronic drinking system. Two periods were used to identify the optimal interval (the baseline set), and the other 2 periods were used to validate the interval (the validation set). The occurrence of a replacement identified by video was paired with the interval between drinking events of 2 cows at the same electronic drinker to identify the interval that best predicted replacement events. Based on the low prevalence of replacement events compared with nonreplacement events, the interval with the lowest number of false positives was considered optimal. Of the 3 potential metrics, the point on the receiver operating characteristic curve where Se and Sp were roughly equal yielded the fewest false positives. The optimal interval to identify replacements at the drinker was ≤29 s, with 82% Se and 83% Sp in the baseline set. This interval was found to have 85% Se and 89% Sp when applied to the validation set. These results indicate that social competition between dairy cows at the drinker can be accurately measured with an electronic drinking system.
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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 | 0.001 | 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".