Managing vessel-generated underwater noise to reduce acoustic impacts to killer whales
Bibliographic record
Abstract
Launched in 2014, the Enhancing Cetacean Habitat and Observation (ECHO) Program, led by the Vancouver Fraser Port Authority, is a regional, collaborative program designed to better understand and reduce the cumulative effects from commercial shipping activities on at-risk whales. The program advances a number of research projects and operational initiatives, with a key focus on reduction of vessel-generated underwater noise to support recovery of the endangered Southern Resident Killer Whale (SRKW). To reduce vessel noise in key killer whale habitat, the ECHO Program manages seasonal voluntary slowdowns of large commercial traffic and lateral displacement of inshore traffic, and advances projects to better characterize vessel-generated noise. Determining the efficacy of vessel noise reduction efforts requires careful consideration of confounding factors to assess acoustic reductions, and estimate the resulting benefits to SRKW. This presentation will highlight results of noise mitigation efforts conducted to date, and identify some of the technical challenges faced in the acoustic analysis, including decisions on data filtering techniques. Methods used by the ECHO Program for evaluating potential effects of noise exposure on killer whale behavior and foraging will also be discussed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".