Validation of a system for monitoring feeding behavior in beef cattle1
Bibliographic record
Abstract
The objective of this study was to evaluate the use of an electronic radio-frequency-identification-based system (GrowSafe System Ltd., Airdrie, Alberta, Canada) to measure feeding behavior traits in beef cattle fed a high-grain diet. Feeding behavior data were recorded by the GrowSafe system and time-lapse video using 10 heifers over a 6-d period. Observed bunk visit (BV) and meal event data (frequency and duration) were compared with electronic feeding behavior data generated by the GrowSafe system at 5 parameter settings (MPS; 30, 60, 100, 150, and 300 s), which are used to define the maximum duration between consecutive electronic identification recordings to initiate a subsequent BV event. A random coefficient model was used to compare video and electronic data using orthogonal contrasts. Video data were regressed on the electronic feeding behavior data to obtain an estimate of precision (r(2)) and other statistical estimates, including mean square error of prediction and concordance correlation coefficient, to access the adequacy of the electronic system predictions. The variation in MPS values affected BV data, but not meal event data. Electronic meal frequency and duration data were not different (P > 0.50) from observed values, and were not affected by electronic MPS values. The optimal MPS value for prediction of BV and meal event frequency and duration traits was 100 s. Our evaluation indicated the GrowSafe system 4000E was able to predict BV and meal event data when the 100-s MPS was used to analyze the feeding behavior data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".