Toward efficient shipping noise probability density function estimation using sea-lane source decomposition and probability theory
Bibliographic record
Abstract
One goal of the Canadian Ocean Protection Plan (OPP) is to understand the potential effects of shipping noise on endangered whale species in order to mitigate them. Shipping noise environmental impact risk assessment requires the understanding of large scale, high-resolution time-space shipping noise distributions. The computation of such shipping noise probability density functions (pdf) requests considerable computing resources, especially when propagation occurs in complex and varying environments like shallow waters, canyons, or fjords. Besides, input parameters variability and uncertainties analyses require multiple hindcast, nowcast or forecast scenarios to be run when using a direct Monte-Carlo approach. In order to reduce the computation effort, sea-lane shipping traffic decomposition and probability theory are jointly used to derive shipping noise probability density functions with a logarithmic complexity algorithm, as opposed to the linear complexity of direct Monte-Carlo methods. First, a theoretical model is derived for straight shipping routes using simplified logarithmic propagation, validated with numerical examples, and used to perform a sensitivity analysis of shipping noise pdf to speed and route closest point of approach. The improvement in numerical efficiency is shown on a more realistic four sea-lane case scenario mimicking part of the summertime St. Lawrence estuary traffic. Eventually, in situ measurements will be available for comparison.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".