Source levels and communication-range models for harp seal (Pagophilus groenlandicus) underwater calls in the Gulf of St. Lawrence, Canada
Bibliographic record
Abstract
Harp seals ( Pagophilus groenlandicus (Erxleben, 1777)) produce underwater call types during the breeding season that are thought to be important for reproductive behaviours and herd formation. Underwater calls were recorded in the Gulf of St. Lawrence, Canada, in March 2007. A four hydrophone array system determined the locations of nearby calling seals and call source levels (amplitudes at 1 m from the seal). Source levels ranged from 103 to 180 dB re 1 µPa-m and mean values per call type ranged from 129 to 151 dB re 1 µPa-m with considerable overlap between call types. Short-range sound transmission losses under the ice were variable. Theoretical communication-range models were constructed under quiet (0 sea state, transmission-loss pattern of 20 log range) and noisy (herd noise, transmission-loss patterns of 15, 17.5, or 20 log range) conditions. Monte Carlo models for the calls for a quiet sea indicated median distances of 0.5–5.5 km (maximum 80 km). Communication distances in the presence of other calling seals dropped to 0.03–0.5 km (maximum 15 km) for dB loss = 20 log range but were longer under different spreading-loss patterns. Communication ranges are significantly influenced by call source levels, background noise, and in situ sound transmission patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".