Ocean noise contributors in southern resident killer whale habitat
Bibliographic record
Abstract
The contributions of different marine sectors to underwater noise pollution within endangered Southern Resident killer whale (SRKW) habitat were investigated using a soundscape model for continental shelf waters off British Columbia and Washington State. Inputs to the model included Automatic Identification System (AIS) vessel traffic data, aerial survey estimates of non-AIS traffic, and an extensive database of underwater vessel noise measurements. Model results suggested that roll-on-roll-off ferries had the greatest region-wide contribution to broadband sound pressure level (SPL), followed by container ships, bulk carriers, anchored cargo vessels, and oil tankers. Fishing and recreational vessels had large seasonal contributions in two killer whale masking bands, after accounting for non-AIS vessel presence. Monthly-average SPL from vessels exceeded wind-driven ambient sound by >10 dB throughout most of the study area. These findings highlight the potential impact of vessel activity on acoustic habitat quality for SRKW and other marine animals. Soundscape map of monthly-average SPL in the killer whale communication band for July 2022. • Ro-ro ferries and deep-sea cargo vessels were the main sources of broadband noise in SRKW habitat. • Anchored cargo vessels were the fourth-ranked contributor within SRKW habitat. • Tugs, fishing, and recreational vessels were significant contributors to SRKW masking bands. • Most fishing, and recreational vessels were not captured by AIS. • Vessel-generated SPL exceeded natural ambient by >10 dB throughout SRKW habitat.
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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 | 0.000 |
| 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) | 0.001 | 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".