A statistical representation of oil spill fate in the Salish Sea (Part 2)
Bibliographic record
Abstract
We use a novel approach that combines Automatic Identification System (AIS) ship traffic data, state regulated oil transfer data, and a suite of numerical models to statistically represent the risk of spilled Alaska North Slope Crude, Bunker-C, and Marine Diesel under a variety of environmental conditions in an estuarine environment off the northeastern Pacific Ocean. We show the statistics of fate and transport outcomes based on 10,000 MOHID oil spill model simulations with currents, winds, and waves between January 1, 2015 and December 31, 2018. Each of the 10,000 oil spill scenarios was run individually and includes weathering from biodegradation, dissolution, dispersion, emulsification, evaporation, and spreading. Our pioneering approach captures statistical variability in seasonality, vessel traffic, spill locations, and oil types. We show that heterogeneity of 3D circulation in an estuarine environment, combined with marine traffic "footprints", creates regionally-variable signatures of the timing, likelihood and type of potential oiling.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".