Validation of proximity loggers to record proximity events among beef bulls
Why this work is in the frame
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Bibliographic record
Abstract
Social behavior in cattle can be measured by how often and for how long they interact with each other. This information can be used to guide management decisions, identify sick animals, or model the spread of diseases. However, visual observation of proximity events is time-demanding and challenging, especially for rangeland cattle spread over a large area. Although proximity loggers can potentially overcome these challenges remotely, it is unknown how accurate these devices are in recording proximity events among beef bulls. The objectives of this study were: 1) to determine the accuracy of Lotek LiteTrack LR collars with built-in proximity loggers to identify proximity events among bulls and 2) to determine the accuracy of Lotek LiteTrack LR collars to identify proximity events between bulls wearing collars and bulls wearing the Lotek V7E 154D ear tag proximity transmitter. Collars were deployed in 12 bulls in 2021 (Experiment 1), and 10 bulls (5 collars and 5 ear tags) in 2023 (Experiment 2). Videos were recorded of bull behavior in both years to compare proximity observed to proximity recorded by the loggers. Sensitivity (Se), specificity (Sp), precision (Pr), and accuracy (Ac) were calculated after computing true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN). The interquartile range method was used to detect outliers. As collars work as both a transmitter and receiver in Exp. 1, reciprocity was assessed by the Concordance Correlation Coefficient (CCC) as an indirect measure of reliability. In Exp. 1, most observations were TN (95.13%), followed by FN (4.11%), TP (0.70%), and FP (0.06%). A high Sp (median = 1.0; 95% CI = 1.0 to 1.0), Pr (1.00; 0.72 to 1.0), and Ac (0.96; 0.95 to 0.97), and low Se (0.10; 0.06 to 0.21) were observed. A high reciprocity agreement (0.93; 0.89 to 0.96) was also observed. Likewise, in Exp. 2 most observations were TN (85.05%), followed by FN (9.94%), TP (4.36%), and FP (0.65%), while high Sp (0.99; 0.99 to 1.0), Pr (0.89; 0.80 to 0.92), and Ac (0.95; 0.81 to 0.95), and low Se (0.35; 0.24 to 0.61) was observed. The Pr of two loggers in Exp. 1 and Pr and Ac of one logger in Exp. 2 were considered outliers. In conclusion, both proximity loggers demonstrated high precision, specificity, and accuracy but low sensitivity in recording proximity among beef bulls. Therefore, these characteristics should be considered when deciding whether to use these devices or not.
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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 | 0.001 |
| 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 it