The ECHO Program: Key learnings at 5-year anniversary of vessel slowdown for at-risk whales off BC's southern coast
Bibliographic record
Abstract
The Vancouver Fraser Port Authority-led Enhancing Cetacean Habitat and Observation (ECHO) Program is a regional collaborative initiative to better understand and reduce the cumulative effects of commercial shipping activities on at-risk whales along BC's southern coast. Bringing together over 100 U.S. and Canadian partners and advisors from across government, the marine transportation industry, Indigenous communities, scientists, and environmental groups. The ECHO Program advances research and implements voluntary seasonal initiatives that encourage ship operators to slow down or stay distanced while transiting through key foraging areas of the endangered southern resident killer whale (SRKW) population. In 2020, these voluntary initiatives achieved record-breaking participation rates that resulted in a nearly 50% reduction in sound intensity in key SRKW habitat areas. In addition to leading voluntary seasonal underwater noise reduction efforts for the last five years, the ECHO Program spearheads research and education efforts to better understand underwater noise and inform the development and adoption of vessel noise-quieting technologies. The ECHO Program's presentation will summarize some of the key insights and lessons learned from the program's voluntary initiatives and research projects to date, including: results of its underwater noise reduction efforts in the Salish Sea; trends in ambient noise and mammal presence in the area; results of ongoing studies investigating how factors such as vessel traffic, currents, water temperature, and weather affect ambient underwater noise and which vessel design characteristics contribute to underwater noise emissions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".