Marine mammal noise exposure criteria: Initial scientific recommendations.
Bibliographic record
Abstract
An expert panel reviewed the expanding literature on marine mammal (cetacean and pinniped) auditory and behavioral responses to sound exposure to develop comprehensive, scientifically based noise exposure criteria [Aquatic Mammals 33(4)]. They used precautionary extrapolation procedures to predict exposure levels above which adverse effects (both physical and behavioral) could be expected. Due to the paucity of data on long-term exposures, criteria were developed for single exposure events only. Marine mammals were broken into functional hearing groups. Exposure types were lumped into three broad classes (single pulses, multiple pulses, and nonpulses). Levels estimated to induce permanent noise-induced hearing loss were determined for each of 15 sound type/animal group combinations. For example, injury criteria for pinnipeds in water exposed to multiple pulses were 186 dB re 1 μPa2 -s (weighted SEL) and 218 dBpk re 1 μPa (unweighted peak SPL). Discrete behavioral disturbance thresholds could only be determined for exposure to single pulses. For other exposures, available data on behavioral responses were ranked by severity and significance. This severity scaling and the resulting conclusions will be described. New research required to improve criteria and to assess cumulative and ecosystem-level effects will also be considered, along with current policy and/or regulatory applications.
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 | 0.035 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.013 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".