Files used in a Monte Carlo analysis of oil spill risks in the Salish Sea
Bibliographic record
Abstract
This dataset contains input files, configuration parameters, parameter statistics, and aggregated model outputs generated for a Monte Carlo analysis of oil spill risk in the Salish Sea. The dataset was used to develop thousands of hypothetical oil spill scenarios based on observed vessel movement patterns in 2018, recorded oil transfer operations in 2018, and historical spill data. It includes sets of 10,000 synthetic spill scenarios as well as underlying data sources used to generate the statistics from which spills were derived. The files are organized into folders that include: aggregated model output from a set of 10,000 spills, sets of spill scenarios generated by our method, YAML configuration files, gridded "Vessel Time Exposure" maps in GeoTIFF format, and annotated spreadsheets with information used to inform methodology. The dataset is the result of an interdisciplinary effort to more comprehensively represent oil spill risks in the coastal waters between Vancouver Island and the mainlands of British Columbia and Washington State. It reflects a detailed integration of vessel traffic data, empirical spill records, and operational infrastructure characteristics. Due to licensing constraints, AIS ship track data from SPIRE Maritime is excluded but referenced. Please see the companion articles “A statistical representation of oil spill fate in the Salish Sea (Parts 1 & 2)” published in Marine Pollution Bulletin (Mueller et al., 2025). Source code used for simulation and analysis is openly available on GitHub (https://github.com/MIDOSS/MuellerEtAl_MIDOSS_paper) and archived via Zenodo (https://doi.org/10.5281/zenodo.10939119).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.028 |
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".