The effects of vessel slowdowns on foraging habitat of the southern resident killer whales
Bibliographic record
Abstract
The Enhancing Cetacean Habitat and Observation (ECHO) Program, a collaborative program led by Vancouver Fraser Port Authority, coordinated voluntary slowdowns for commercial shipping traffic in Haro Strait in the summers of 2017 and 2018, to better understand the relationship between vessel speed and source level, and how this may effect noise levels and foraging of the southern resident killer whales. With a speed target of 11 knots in 2017, a 61% participation rate of piloted ships was reported, resulting in a median broadband ambient noise level reduction of 1.7 dB re 1μPa, and a 22% reduction in “lost foraging time”, compared to baseline traffic conditions. In 2018 the slowdown aimed to increase participation by increasing target speeds to 15 knots and 12.5 knots, based on vessel type, thus decreasing operational and economic barriers to participation. This resulted in 87% of piloted vessels reporting participation, an ambient noise reduction of 1.5 dB re 1μPa, and a modelled reduction in “lost foraging time” on the order of 15%. These slowdown efforts indicate that voluntary operational measures by the shipping industry may be an effective means of reducing underwater noise in the foraging habitat of southern resident killer whales.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".