Integrating ocean soundscape modelling and mapping into marine environment quality assessment and marine spatial planning
Bibliographic record
Abstract
As on land, underwater anthropogenic noise and its potential impacts on marine ecosystems have been a growing concern in the past three decades. Initially focusing on louder noise sources, acute physical and behavioral impacts on marine mammals and commercial fish, governmental agencies started to integrate soundscapes into marine spatial planning. However soundscape science has to deal with a large number of metrics and variables (time, space, frequencies, species, types of impacts, sound sources,…) and uncertainties to be able to bring a scientifically robust and reliable support to decision making process. This is a real challenge to integrate and communicate to a vast diversity of stakeholders. To address this challenge, we present a study of the impact of shipping noise on the St Lawrence Estuary and Gulf ecosystems and in particular on endangered marine mammals. The methodology uses probabilistic underwater acoustic modelling to produce 3D-maps of acoustic-field statistics and risk of impacts at daily, weekly, monthly and annual scale over few year-cycles. Those maps are then fed into a web application capable of handling terabytes of geospatial raster’s which allows to produce statistics on user-defined area interactively in order to explore and support collaborative decision making process between marine spatial planner and stakeholders. Details on probabilistic methodology and practical examples will be shown, with concluding remarks on gaps and remaining challenges.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".