Applying technical guidance from the USA for management of impacts of anthropogenic noise on wildlife in other countries: the Canadian context
Bibliographic record
Abstract
Technical Guidance from the US National Marine Fisheries Service recommends Federal agencies use estimated thresholds for peak sound pressure levels and weighted cumulative sound exposure levels for the onset of permanent (and temporary) hearing threshold shifts in marine mammals. These dual metrics were developed to inform impact assessments within the US legal landscape. Despite its merits, the Technical Guidance contains uncertainties due to limited data on marine mammal hearing and auditory response to noise. The underlying assumptions about how representative existing data are for all marine mammal species also create limitations in the applicability of the Technical Guidance. These limitations warrant consideration before the Technical Guidance can be applied effectively in other jurisdictions with different legal standards. Using the Canadian legal framework as a working example, we found that many Canadian species are underrepresented in the dataset informing the Technical Guidance. The Technical Guidance also does not address all relevant noise impact types. Thus, the Technical Guidance alone cannot address all Canadian legal standards and, if the Technical Guidance is incorporated, some adjustments to the criteria within may be needed to meet the precautionary requirements of many Canadian legal standards.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".