A statistical representation of oil spill fate in the Salish Sea based on AIS ship traffic, oil transfer data, and a Monte Carlo model framework.
Bibliographic record
Abstract
Bequeathing future generations a Salish Sea that is absent of oil spill impacts requires good information on the spatial distribution of the most likely oil spill scenarios and their consequences in order to help develop effective plans for oil spill prevention and response. We developed a data-informed modeling framework for generating statistical maps of oil spill fate in the Salish Sea to help provide this information. Oil spill location, month and volume are randomly generated from a year’s worth of AIS ship track data that was organized into vessel time exposure maps for seven different vessel classifications. For oil cargo vessels, we use the time attribution in AIS ship tracks to create voyages that identify the ship’s origin and destination. Ships that are either identified as having U.S. origin or destination or that are in U.S. waters and without a Canadian origin or destination are attributed with an oil type that is determined by the Washington State Department of Ecology oil transfer data. We randomly select a spill day, hour and year between January 1, 2015 and December 31, 2018 to capture a wide range of spill conditions. Our 7-day spill scenarios use currents, winds and waves that are predicted by the SalishSeaCast, HRDPS and WW3 models, respectively. We generated 10,000 random oil spills with our Monte Carlo simulation and predicted oil dispersion, emulsification, dilution, biodegradation, beaching and advection for these spills using a modified version of the MOHID oil spill model. In this talk, we will detail the design of the Monte Carlo simulation and present maps of the likelihood of oil presence and volume based on region and oil type.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".