Antimasking aspects of harp seal (<i>Pagophilus groenlandicus</i>) underwater vocalizations
Bibliographic record
Abstract
Underwater sounds are very important in social communication of harp seals (Pagophilus groenlandicus) because they are the main means of long- and short-distance communication. Individual harp seals must try to avoid being masked and emit only those calls that will benefit them. Underwater vocalizations of harp seals were recorded during the breeding season. The physical characteristics associated with antimasking attributes of 16 call types were examined. Rising frequency or increasing amplitude within calls were not common. Most of the calls ended abruptly (range 145-966 dB/s), but call onset was more gradual. At high calling rates (95.1-135 calls/min) there were significantly more calls overlapping temporally than at medium (75.1-95 calls/min) or low (35-75 calls/min) calling rates, but even at the highest calling rates, 79.1% of the calls were not overlapped. When 2, 3, or 4 calls overlapped, there were significantly fewer frequency separations of less than 1/3 octave than would be expected by chance. This is important because sounds that are separated by less than 1/3 octave likely mask each other. When 2-4 calls are occurring simultaneously, only 4.5% to 14.2% are masked by virtue of being within 1/3 octave from their nearest neighbor. None of the overlappping calls was of the same type. This suggests that the seals are actively listening to each other's calls and are not randomly using the different call types. Harp seals use frequency and temporal separation in conjunction with a wide vocal repertoire to avoid masking each other.
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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".