Analysis of systematic source level measurements of small vessels
Bibliographic record
Abstract
Improving our knowledge of how sound impacts marine mammals is particularly important where the spatial distributions of vessels and marine mammals overlap, as exemplified by the critical habitat for the endangered Southern Resident Killer Whale (SRKW). In this study, two acoustic recorders were deployed in transboundary Haro Strait (British Columbia, Canada and Washington State, USA) from July to October 2017 to measure sound levels produced by whale-watching vessels and other small boats. During this period, 20 different volunteer vessels were assessed operating at a range of speeds—nominally 5 knots, 9 knots, and cruising speed. The measurement protocol was designed based on ANSI S12.64-2009. For all vessels, we observed positive correlations between source levels and speed; however, the speed trends (slope of curves) were not as strong as those of large commercial vessels. Mean source levels were computed for each vessel type in the broadband frequency range (0.05–64 kHz), the SRKW communication band (0.5–15 kHz), and the SRKW echolocation band (15–64 kHz) at each speed. Here we discuss how source levels were affected by vessel speed, hull shape and propeller type, as well as the positive and negative aspects of the protocol design.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".