Antimasking strategies of underwater vocalizations and hearing abilities of polar seals
Bibliographic record
Abstract
Detection of underwater vocalizations by polar seals is limited by their auditory sensory abilities and external masking noises from abiotic (meteorological and ice noises) and biotic (conspecific calls) sources. Attributes that present the antithesis of masking noise characteristics are thought to enhance detectability of calls. Some proposed anti-masking strategies such as call repetition/rhythm patterns are supported by evidence of lower detection thresholds (1–5 dB), while others are not (e.g., abrupt onset and offset of calls). For frequency swept calls, downsweeps have lower detection thresholds (1–5 dB) than upsweeps. The majority of frequency swept calls (greater than 0.1 oct) of bearded (Erignathus barbatus), Weddell (Leptonychotes weddellii) and harp (Pagophilus groenlandicus) seals are downsweeps (89%, 86% and 63%, respectively). Temporal and frequency separation, call lengthening, and directional clues also reduce masking effects. Diverse call repertoires and calling behaviors of polar seals (e.g., courtesy rule) appear to have evolved characteristics that reduce the effects of abiotic and biotic masking. Characteristics of many anthropogenic underwater noises differ from sounds produced in nature. To estimate masking effects of anthropogenic noise on phocid communication, it is important to determine if the man-made noises are defeating existing antimasking strategies.
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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".