Southern Resident killer whale acoustic monitoring and noise measurements to inform noise mitigation and potential management strategies
Bibliographic record
Abstract
SMRU Consulting, in collaboration with the ECHO Program, the San Juan County MRC and DFO, has been monitoring Salish Sea underwater soundscapes for the presence of Southern Resident killer whales (SRKW), to measure ambient noise levels, evaluate the efficacy of noise mitigation measures and inform potential management strategies. This talk will highlight results from four projects across the years 2019–2021. This includes measurements from a cabled observatory at Lime Kiln Point State Park in support of the ECHO Program’s Haro Strait commercial vessel slowdowns; measurements from autonomous recorders at five locations in Burrard Inlet for the ECHO Program and the Tsleil-Waututh Nation to monitor trends in ambient noise and marine mammal presence; measurements from Coastal Acoustic Buoys at eight locations south of San Juan Island in support of San Juan County’s management efforts, and a Coastal Acoustic Buoy for Offshore Wind deployment near Point Roberts to allow DFO to evaluate acoustic technology. This talk will discuss the pros and cons of these four different acoustic systems and highlight trends in the data that may help inform mitigation and management strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".