High-amplitude vocalizations of male northern elephant seals and associated ambient noise on a breeding rookery
Bibliographic record
Abstract
Northern elephant seals (Mirounga angustirostris) are massive, land-breeding marine mammals that produce loud, stereotyped calls during annual breeding seasons. To determine vocalization source levels emitted by competing males on a mainland breeding rookery, aerial calls were measured on-axis at 1 m from adult males using three different sound pressure level metrics. Time-averaged (1 min) ambient noise was also measured under variable environmental and social conditions. Results indicate that male northern elephant seals emit high amplitude airborne calls with little variation in call amplitude. Mean source levels ranged from 98 to 114 dB re: 20 μPa [root-mean-square (rms) -fast], 102-116 dB re: 20 μPa (rms-impulse), and 120-131 dB re: 20 μPa (peak) and average standard deviations for all metrics were <2.3 dB. Further, these seal rookeries exhibit high variability in ambient noise (in terms of both spectrum and amplitude) from biotic and environmental sources. Finally, males sampled did not adjust call amplitude to compensate for higher background noise levels and thus did not exhibit a Lombard effect. These findings reinforce the view that the remarkable vocalizations of male northern elephant seals serve as rigid and powerful signals that convey individual identity within noisy breeding colonies rather than as honest indicators of size, status, or motivation.
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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".