Regulating underwater sound in Eastern Canada: An operational perspective from the Department of Fisheries and Oceans
Bibliographic record
Abstract
Fisheries and Oceans in Canada are responsible for policies and programs that support economic, ecological and scientific interests in Canada’s marine waters. Central to the Department’s mandate is the conservation of living marine resources, including mammals. Canadian regulations, permits and approval processes will be discussed with an emphasis on acoustic devices and related noise issues on the Scotian Shelf. Commercial seismic research on the Shelf has added considerably to the sound dependent marine research typically undertaken by government and naval scientists. This increase in noise has prompted calls for cumulative environmental assessments that address additive and synergistic effects. Existing mitigation measures include sensitive area avoidance and coordination to avoid spatial and temporal overlap. Operations are restricted near the Sable Gully, a large canyon recognized as the most significant cetacean habitat on the Scotian Shelf. Evolving nonregulatory approaches include codes of conduct, precautionary buffer zones and voluntary compliance with operational guidelines. Work conducted by Defence Research Establishment Atlantic on historical levels of ambient noise has been especially instructive in these matters. Additional measurement and modeling expertise is needed to help establish safe operating distances and environmental quality standards that could be applied to all anthropogenic sound sources near the Sable Gully.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".