Development of the calf grimace scale for pain and stress assessment in castrated Angus beef calves
Bibliographic record
Abstract
Grimace scales have been used to assess pain in various animal species. This study aimed to develop the calf grimace scale (CGS), evaluate its responsiveness and the effect of external factors (change of environment and dam separation, and restraint) on CGS. Sixty-nine Angus calves, 6-8 weeks old, were randomly allocated into castrated (n = 34) and sham castrated (n = 35) groups. Images were extracted from videos pre- (M1-M4), during- (M5), and post-castration/sham castration (M6, M7). Six facial action units (FAUs) were identified: ear position, orbital tightening, tension above the eye, nostril dilation, straining of chewing muscle, and mouth opening. Final CGS median scores increased after castration (P < 0.001) for both non-restrained (M7 versus M2) and restrained (M6 versus M3) calves, indicating scale responsiveness. Final CGS median scores increased (P < 0.001) when calves were subjected to external factors before castration (M1 [baseline] versus M2 and M3). However, there was no difference (P > 0.05) in CGS median scores before and after sham castration, regardless of restraint (M3 versus M6, and M2 versus M7), indicating that the external factors may have reached a maximum effect. The CGS is composed of six FAUs, responsive to acute pain and can identify stress unrelated to pain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".