Evaluating the predictive strength of underwater noise exposure criteria for marine mammals
Bibliographic record
Abstract
The aim of underwater noise exposure criteria in a regulatory context is to identify at what received levels noise-induced effects are predicted to occur, so that those effects may be appropriately considered in an evaluation or mitigation context under the respective regulatory regime. Special emphasis has been given to hearing related impairment of marine mammals due to their high sensitivity to and reliance on underwater sound. Existing regulations of underwater noise show substantial qualitative and quantitative discrepancies. A dataset acquired during an experiment that induced temporary threshold shift (TTS) in a harbor porpoise (Phocoena phocoena) from Lucke, Siebert, Lepper, and Blanchet [(2009). J. Acoust. Soc. Am. 125, 4060-4070] was reanalyzed to see if various exposure criteria predicted TTS differently for high-frequency cetaceans. This provided an unambiguous quantitative comparison of predicted TTS levels for the existing noise exposure criteria used by regulatory bodies in several countries. The comparative evaluation of the existing noise exposure criteria shows substantial disagreement in the predicted levels for onset for auditory effects. While frequency-weighting functions evolved to provide a better representation of sensitivity to noise exposure when compared to measured results at the criteria's onset, thresholds remain the most important parameter determining a match between criteria and measured results.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".