PSV-16 Validation of an algorithm to assess feedbunk replacement events in beef cattle using an electronic feeding system
Bibliographic record
Abstract
Abstract Visual observations of social behavior and dominance relationships in cattle have been used to examine associations with productivity and well-being. This method is time consuming limiting the number of animals that can be evaluated. The objective of this study was to validate an algorithm to quantify feedbunk replacement events using data from an electronic feeding system. Crossbred beef steers (n = 20) fed a grower diet were housed in 1 of 2 pens each equipped with 3 electronic feedbunks (GrowSafe Systems) and video recorders. A trained video observer recorded all feedbunk replacement events and other agonistic activities at the feedbunk over a 4-d period (24 h/d). The electronic feeding system recorded the start and end timestamps of bunk visit (BV) events for each animal. An algorithm was developed to determine BV events deemed to be replacement events, defined as a BV event when an actor animal displaced a reactor animal from the feedbunk and occupied the same feeder within a specified period of time (replacement criterion). We calculated the recall and precision corresponding to replacement criterions from 1 to 60 s, and the optimum replacement criterion was determined to be between 18 and 20 s. The recall, precision and F-score of the algorithm using this replacement criterion were high (on average > 0.75). Furthermore, a replacement competition index was computed as a proxy for competitive feedbunk behavior, calculated as the number of actor-initiated replacement events divided by the total number of replacement events for each steer. Using Spearmans rank correlation we found high correlations (r > 0.7; P < 0.05) between the electronic and observed indices. These preliminary results demonstrate the potential of the GrowSafe system to quantify feedbunk replacement events for confined beef cattle, providing opportunities to evaluate associations between competitive feedbunk behavior and economically relevant traits.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".