Modelling ship movements: Applications for noise exposure to the marine ecosystem
Bibliographic record
Abstract
Ship-source marine noise is an emerging issue that is increasingly shown to interfere with marine mammals, fish, potentially marine birds and other animals. The exposure to ship-based noise is expected to increase in the Salish Sea as marine vessel activity increases due to planned port expansions and new marine terminal construction on Canada’s Pacific coast. Increasingly, government and industry are required to take operational and strategic mitigation measures without reliable and comprehensive data and analysis to inform those decisions, and in the absence of national guidelines. The goal of this research has been to explore and improve the utility and modelling of ship traffic, based on AIS and other data, as an indicator of noise to enable government, industry and, even individuals, make better decisions to mitigate marine noise impacts. Specifically, the research addresses the following three questions: 1) How can we build a reliable, comprehensive spatio-temporal model of vessel movement? 2) How can we confidently associate noise with marine vessels to understand cumulative noise exposure? 3) How can we integrate vessel traffic models and noise exposure models with decision making and outreach? To accomplish this goal a multidisciplinary team of researchers has been assembled to tackle these research questions for each of the projects three study areas: Sach’s Harbour in the Arctic, SGaan Kinghlas Bowie Seamount on the west coast of Haida Gwaii and the Salish Sea. Here we show the results of vessel traffic modelling for the Salish Sea, the most heavily trafficked of all three areas, and still facing further increases in shipping levels due primarily to advances on the previously planned port expansion in Vancouver.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".