Do vocalizations tell us anything about animal welfare? Les vocalisations nous communiquent-elles un message sur le bien-être des animaux?
Bibliographic record
Abstract
621 A nimals’ vocalizations are a common sound in veterinary practice, and research is being done on the relationship between vocalizations and animal welfare. Most of this research concerns farm animals (1,2), and research in different domestic species will be very relevant to veterinary practice by indicating how a vocalizing individual is faring and how its vocalizations affect the welfare of conspecifics (2). The latter point is a consideration wherever animals are kept in groups (veterinary hospitals, abattoirs, farms, animal shelters). In terms of welfare assessment, vocalizations have the advantage of being quantifiable (duration, rate, and frequency); of reflecting inner states, such as fear (1,2); and, in some situations, of being reliably related to degree of need (“honest signals”) (3). Vocalizations are potentially more accurate than some other indices of welfare, such as heart rate, which increases following both pleasant and unpleasant events, and are very sensitive to moderate stress (1). Before vocalizations can be incorporated into routine welfare evaluation, more research is needed on the components of the sounds and on their context and environment (1). In veterinary practice, pain-related vocalization and social vocalization have particular relevance. Some of the research in these areas, much of which has been conducted in Canada, is outlined in this article.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".