Acoustic monitoring of marine environmental quality: An example from the Estuary and Gulf of St. Lawrence
Bibliographic record
Abstract
As a part of the Canada’s Ocean Protection Plan, Fisheries and Oceans Canada has joined the efforts to better understand and monitor the effects of anthropogenic noise on marine environmental quality. Since 2017, underwater acoustic observatories were put in place across endangered whale habitats leading to the acquisition of big underwater acoustic dataset to process and analyze. In the Estuary and Gulf of St. Lawrence, underwater noise has been continuously monitored at 13 locations (6 to 10 simultaneously) between 2018 and 2023 at sampling rate up to 256 ksps in order to better understand the effect of shipping noise on marine environmental quality of the endangered St. Lawrence estuary beluga habitat. In this presentation, the data analysis pipeline from in situ sampling to processing is detailed, including recording schemes, data quality and control, soundscape cube, source separation, multi-scale statistics on noise levels and risk of impacts on habitat quality and visualization. These steps are used to identify, characterize and quantify daily to interannual spectral variability of underwater noise and their relationship with local environmental forcings such as shipping, wind, ice, tides, and currents at targeted locations. Ultimately, these results are used to provide support to (1) marine conservation and spatial planning initiatives from DFO and the Saguenay St. Lawrence Marine Parc; and (2) assess the predictability power of the outputs of soundscape modeling.
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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 | 0.001 | 0.002 |
| 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".