Resonance plays a secondary role in amplifying underwater vocalizations of Weddell seals (<i>Leptonychotes weddellii</i>)
Bibliographic record
Abstract
Abstract During the breeding season, male Weddell seals (Leptonychotes weddellii) defend underwater territories using high amplitude trill vocalizations (some >190 dB re 1 μPa‐m). The source‐filter theory states that the characteristics of vocalizations are a product of both the sound produced at the source and the effects of soundwave reflection within the vocal tract. This study aimed to determine the relative influence of reflection (resonance). Weddell seal trills (tonal descending frequency sweeps ranging from >20 to <0.1 kHz) offered a unique opportunity to look for resonance in the spectral patterns, appearing as periodic peaks in amplitude over short frequency intervals. Additionally, distributions of frequencies with absolute maximum amplitude (FMA) were tested against random distributions to look for frequencies where FMAs occur more frequently than by chance. All methods exhibited evidence of resonance at 0.45–0.56 and 2.25–2.83 kHz. These frequencies would resonate in closed‐at‐both‐ends tubes with lengths of 32–40 and 6–8 cm, respectively. These lengths approximate the likely sizes of the trachea and larynx or pharynx respectively. The amplitudes of the underwater calls of Weddell seals primarily result from the amplitude generated by the sound source in the larynx with some additional amplitude associated with resonance in the vocal tract.
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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) | 0.002 | 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".