Opportunistic ship source level measurements in the Western Canadian Arctic
Bibliographic record
Abstract
Increased ship traffic due to climate change increases underwater noise in the Arctic. Therefore, accurate measurements of underwater radiated noise are necessary to map marine sound and quantify shipping's impact on the Arctic ecosystem. This paper presents a method to calculate opportunistic source levels (SLs) using passive acoustic data collected at six locations in the Western Canadian Arctic from 2018 to 2022. Based on Automatic Identification System data, acoustic data, and a hybrid sound propagation model, the SLs of individual ships were calculated within a 5 km radius of each measurement site. A total of 66 measurements were obtained from 11 unique vessels, with multiple measurements from the same vessel type contributing more SLs. For vessels with propeller cavitation, measured SLs correlated positively with vessel parameters, such as speed and length. SL and speed did not correlate well for vessels without propeller cavitation. The JOMOPANS-ECHO SL model produced good agreement with measured SL for certain ship types (container ships, a tanker, and a passenger vessel). However, significant differences between measurement and model are evident for certain polar-class ships that travel in the Arctic, indicating that more controlled SL measurements are needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".