Benefits of voluntary vessel slowdowns to acoustic space reduction for killer whales
Bibliographic record
Abstract
Underwater noise from marine vessels can reduce the acoustic space that is available to marine mammals for performing critical life functions. Likewise, measures such as speed reductions that decrease vessel radiated noise can limit the extent to which acoustic space is affected by marine traffic. To investigate the benefits of voluntary summer vessel slowdowns in Haro Strait and Boundary Pass, the Enhancing Cetacean Habitat and Observation (ECHO) program commissioned a modelling study of acoustic space reduction which focused on two frequency bands relevant to resident killer whales: a communication band (0.5–5 kHz), using a metric termed Listening Space Reduction (LSR) and an echolocation band (15–100 kHz), using a metric termed Echolocation Space Reduction (ESR). The LSR and ESR metrics calculate a relative percent change from the maximum listening or echolocation space under natural ambient conditions to the reduced space caused by anthropogenic noise. Percent LSR and ESR was calculated in the study area at fine spatial (200 m) and temporal (1 min) resolution for scenarios representing baseline and slowdown traffic conditions. Results of this study demonstrate the benefits of voluntary vessel slowdowns on lost listening and echolocation space for 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".