Strategies for weighting exposure in the development of acoustic criteria for marine mammals
Bibliographic record
Abstract
The Noise Exposure Criteria Group has been developing noise exposure criteria for marine mammals. Although the primary focus of the effort is development of criteria to prevent injury, the Group has also emphasized the development of exposure metrics that can be used to predict injury with accuracy and precision. Noise exposure metrics for humans have proven to be more effective when they account for psychophysical properties of the auditory system, particularly loudness perception. Usually noise is filtered using the A-weighting function, an idealized curve based on the human 40-phon equal loudness function. However, there are no empirical studies to show whether a comparable procedure for animals will improve predictions. The Noise Exposure Criteria Group panel has proposed to weight noise data by functions that admit sound throughout the frequency range of hearing in five marine mammal groupings—low frequency cetaceans (mysticetes), midfrequency cetaceans, high-frequency cetaceans, pinnipeds in air, and pinnipeds in water. The algorithm for the functions depends only on the upper and lower frequency limits of hearing and does not differentially weight frequencies based on sensitivity within the range. This procedure is considered conservative. However, if the human case may be taken as a model, it is not likely to produce precise predictions. Empirical data are essential to finding better estimators of exposure.
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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 | 0.027 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".