Eye white percentage as a predictor of temperament in beef cattle
Bibliographic record
Abstract
Accurately evaluating and selecting for calm temperament in beef cattle is important for economic and animal welfare reasons. Previous studies have shown that eye white (EW) can be a predictor of a multitude of emotions across different situations, but there is little research on the relationship between EW and temperament. The objective of this experiment was to assess the accuracy and reliability of using the percentage of exposed EW as a predictor of temperament in beef cattle. Forty-eight heifers (group 1), 39 bulls (group 2), and 60 steers (group 3) were video-recorded while in a squeeze chute, and 2 still digital images from each animal were selected for EW determination. Chute temperament scores were assigned: 1 (calm) to 5 (agitated). Flight speeds were measured blindly and independently during a subsequent test in which the amount of time it took a solitary animal to pass a handler and travel a specified distance was recorded. The EW area in each image was measured using Sigmascan Pro 5 and was expressed as the percentage of exposed eye area. Each image was analyzed twice to determine tracing repeatability. Pearson correlation coefficients were calculated among 2 images of the same animal, as well as among duplicate readings of the same image to determine animal and tracing repeatabilities. The mean percentages of EW were 30.14 +/- 14.37, 31.43 +/- 14.77, and 28.57 +/- 12.38, and the average percentage accuracy for duplicate image EW measures was 96, 96, and 93 (P < 0.0001) for groups 1, 2, and 3, respectively. The Pearson correlation coefficients for EW percentage and chute temperament scores were 0.674 (P < 0.0001), 0.95 (P < 0.0001), and 0.696 (P < 0.0001), whereas the correlations between EW and flight speeds were 0.415 (P < 0.0001), 0.333 (P < 0.05), and 0.294 (P < 0.01) for groups 1, 2, and 3, respectively. Results from this study indicate that percentage EW in cattle could be used as a quantitative tool with minimal equipment to assess temperament in beef cattle, providing an objective method for temperament selection.
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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 | 0.001 | 0.001 |
| 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 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".