Empirical Weathering Properties of Oil in Ice and Snow
Bibliographic record
Abstract
The objective of this study was to generate experimental data to validate and refine oil spill weathering algorithms for computerized models for spills in ice and snow. Six series of laboratory-scale experiments were conducted over a four-year study: (1) Spreading on Ice and in Snow; (2) Evaporation in Ice and Snow; (3) Slick Thickness on Cold Water; (4) Migration Rates through Brine Channels; (5) Formation of Water-in-Oil Emulsions; (6) Full Spill-Related Characterization of Crude Oil Samples. The experiments were conducted at three facilities: (1) An outdoor test facility near Ottawa, ON constructed using insulated, IBC shipping containers as the test tanks each containing 1 m3 of salt water. (2) An indoor, 11-m³ wind/wave tank at SL Ross in Ottawa, ON. (3) The 10,000-m3 Ohmsett Facility in Leonardo, NJ, outfitted with large-capacity industrial water chillers to ensure freezing water temperatures. Four Alaskan crudes were used: Alaska North Slope, Northstar, Endicott, and Kuparuk.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".