In-ice oil spill trajectory modeling based on a satellite-derived ice drift dataset for the Beaufort Sea
Bibliographic record
Abstract
Knowing where an oil-spill would go is crucial to assess explorations and developments risks and to plan for an optimized and effective clean-up. Having this knowledge is even more crucial for spills in the harsh climate of the hydrocarbon-rich Beaufort Sea where the dominance of harsh ice and darkness during colder seasons make detection and clean-up very challenging. We have modelled and analyzed several in-ice oil spill scenarios for this location and seasons during which the concentration of ice is very high. A satellite-derived ice drift dataset is employed as the driver of the in-ice spills with the assumption that oil only moves with ice. Shallow and deep water spills at different locations, starting at different times and with different spill durations are modelled. Trajectories were modelled assuming that the ice drift dataset is and is not error-free. Uncertainties were modelled through a Monte-Carlo approach. Some of the conclusions follow: (1) the extent of the spill is generally larger when the spill starts on 1 Nov. than when it starts on 15 Dec. (2) deep water spills extend farther than shallow water spills, and (3) shallow water, earlier in the colder winter season, and longer-lasting spills are generally associated with elongated contaminated territorial water boundaries.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".