Exploring the efficiency of a source-level model to predict hydrophone-based received noise levels of the St. Lawrence Estuary’s merchant fleet
Bibliographic record
Abstract
The Enhancing Cetacean Habitat and Observation (ECHO) source-level model was statistically derived using nearly 10,000 vessel transits along Canada’s western coast. Before using this model in other areas, it is important to examine its efficiency in new environments and on other fleets. In this work, hydrophone-based acoustic recordings collected between August 3rd and September 16th of 2022 within the Saguenay-St. Lawrence Marine Park were used to investigate the applicability of the ECHO source-level model to Canada’s eastern fleet. Opportunistic transits of 71 merchant ships near (i.e., ≲ 3 km ) the hydrophone were obtained. As an attempt to reduce computing times in the context of agent-based modeling, the reliability of the proposed analytic seabed critical angle (SCA) method for propagation losses calculations was examined and compared to robust, although time-consuming, numerical methods . Results generally show that the combination of the ECHO source-level model and our best estimates for propagation losses calculations does allow to reliably predict the acoustic footprint of the radiated noise levels by merchant ships in the acoustic module of the Marine Mammal and Maritime Traffic Simulator (3MTSim). However, computational gains could be marginal at best due to the analytic SCA method quickly becoming inconsistent with the numerical approach in range-dependent scenarios that often translate into long-range encounters.
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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 | 0.000 | 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) | 0.000 | 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".