Pain Assessment in Cattle during Castration Using Facial Expressions as a Promising Tool
Bibliographic record
Abstract
Abstract Pain is a significant welfare issue in farm animals including cattle, impacting their wellbeing and normal behaviors. The frequent occurrence of pain in cattle is due to husbandry procedures, injuries, and diseases. Cattle, as a prey species, avoid expressing pain which makes pain identification a challenging issue. Rapid pain recognition is crucial to its effective treatment. Several pain assessment tools have been developed, and some of them rely on observing alterations in animal behaviors such as activity, locomotion, feeding, play, and grooming. Moreover, changes in physiological parameters including blood biomarkers and heart rate have been used as indicators of pain. Furthermore, grimace scales, which rely on evaluating facial expressions of animals, have been developed and proven to be accurate pain assessment tool in multiple species. To reliably identify pain in cattle, it is preferred to employ a combination of diagnostic methods, as no single approach can stand alone in assessing pain accurately. Mitigation strategies become imperative when pain is anticipated during husbandry procedures such as castration. Prompt application of treatment strategies is paramount to avoid transition of acute to chronic pain which is hard to be treated. Information © The Author 2024
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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".