Within-call repetition may be an anti-masking strategy in underwater calls of harp seals (<i>Pagophilus groenlandicus</i>)
Bibliographic record
Abstract
Underwater vocalizations of harp seals (Pagophilus groenlandicus) were recorded in the Gulf of St. Lawrence, Canada, during the breeding season in March of 1999 and 2000. At high calling rates (>95 calls/min) the background noise levels increase and individual calls may be masked. The purpose of the study was to determine if seals increase the number of elements per call in response to higher calling rates by conspecifics. Eight multi-element call types were analyzed. Six narrowband and one of two broadband multi-element call types showed a significant increase in the number of elements per call at higher calling rates. One broadband call type did not show a significant difference among the different calling rates. Our findings suggest that harp seals increase the number of elements per call in many call types to avoid having their calls masked by an increasing number of conspecific vocalizations.
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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 | 0.001 |
| 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) | 0.001 | 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".