Vocal changes as indicators of pain in harbor seal pups (<i>Phoca vitulina</i>)
Bibliographic record
Abstract
Abstract Vocalizations are potential indicators of pain in animals. We recorded and analyzed spectrographically the vocalizations of harbor seal pups (Phoca vitulina) before, during, and after the routine procedures of flipper tagging and microchipping prior to release from a rehabilitation facility in British Columbia, Canada. It is standard practice for these procedures to be done without analgesia. In Experiment 1, we compared vocalizations before and after the procedures (n = 21); in Experiment 2, we compared vocalizations in response to real and sham procedures (n = 10). In Experiment 1, seals produced more vocalizations, and peak frequency was higher, after tagging and after microchipping. In Experiment 2, seals also produced more vocalizations after real but not after sham tagging and microchipping. The average peak frequency was higher after each procedure, but not after each sham procedure. These results suggest that an increase in the number and peak frequency of vocalizations are indicators of pain in seal pups. The results also suggest that analgesia, when feasible, should be considered for harbor seal pups undergoing routine flipper tagging and microchipping.
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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 | 0.000 | 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) | 0.001 | 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".