A statistical representation of oil spill fate in the Salish Sea (Part 1)
Bibliographic record
Abstract
Transport of different oil types as fuel or cargo varies within the Salish Sea and over time. Although many studies have focused on the future health of Salish Sea ecosystems under a warming climate, no study has addressed how market-based dependencies on fossil fuels introduces geographic and temporal variations in ecosystem vulnerabilities. This paper aims to help address this knowledge gap with details of oil transport and oil spill risk in the Salish Sea. Part 1 describes the method that we developed to statistically generate individual oil spill scenarios based on ship traffic data, Washington state oil transfer data, and information on past oil spill events. We examine a set of these 10,000 random spill scenarios, referred to as "Study Spill Set," and we compare this Study Spill Set to other sets of 10,000 spills. The Study Spill Set generated by this Monte Carlo method is the spill set that was used in the model simulations described and presented in a second paper in this special issue, Part 2 (Mueller et al., 2025). While Part 1 focuses on the methods and results from statistically generating 10,000 spill scenarios, Part 2 focuses on the likelihood of oil spill impacts in the Salish Sea based on the fate and transport of these 10,000, statistically-generated spill scenarios. In this paper, we explain the development of our Monte Carlo approach and show that Salish Sea oil spill risks are regionally variable by oil type and spill volume.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".