Hearing thresholds, for underwater sounds, of harbor seals (<i>Phoca vitulina</i>) at the water surface
Bibliographic record
Abstract
High-amplitude impulsive sounds produced by pile driving and airguns may result in hearing damage in nearby seals. By swimming at the water surface, seals may reduce their exposure to underwater sound, as sound pressure levels (SPLs) are often lower just below the surface than deeper in the water column. Seals can make physiological adjustments such that they can switch between having maximum sensitivity for either aerial or underwater sounds. This could mean that hearing sensitivity for underwater sounds is lower when swimming at the water surface (when hearing may be focused on aerial sounds) than when swimming at depth. To investigate this possibility, hearing thresholds of two female harbor seals were quantified psychophysically, while their heads were in the position normally adopted while swimming at the surface. The seals' hearing thresholds at the water surface were similar to each other and to previous measurements made at 1 m depth. When calculating the cumulative sound exposure level for hearing damage assessment, the SPL just below the water surface needs to be measured or modeled, and the proportion of time seals normally swim at the water surface needs to be estimated, to estimate the sound energy that reaches the seals' ears.
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 | 0.000 | 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) | 0.002 | 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".