A COMPREHENSIVE NUMERICAL APPROACH TO PREDICT OIL-MINERAL AGGREGATE (OMA) FORMATION FOLLOWING OIL SPILLS IN AQUATIC ENVIRONMENTS
Bibliographic record
Abstract
ABSTRACT Aggregation between suspended sediment grains and oil droplets, which leads to the formation of agglomerates commonly referred to as Oil-Mineral Aggregates (OMA), is widely acknowledged as a natural process that enhances dispersion of spilled oil in aquatic environments. A comprehensive numerical approach is developed to predict the contribution of OMA formation to the dispersal of spilled oil. The model comprises four modules to calculate maximum size of oil droplets, to predict formation of oil droplets from a slick, to predict formation of sediment floc, and to calculate density of oil-sediment flocs. The inputs of the model are environmental conditions, oil properties and concentration and grain-size distribution of suspended sediments. Sensitivity analysis performed using five crude oils covering a range of viscosities from 8 10−3 to 68 10−3 kg/ms, a kinetic energy dissipation rate from 10−3 to 102 m2/s3, a sediment grain size of 3 μm and a sediment concentration of 250 mg/l showed that formation of OMA is strongly dependent on the oil-water interfacial tension and the kinetic energy dissipation rate. Under breaking wave conditions, the contribution of OMA formation to the dispersal of spilled oil varies between 31 and 97 % depending on characteristics of the individual test oils, in particular oil-water interfacial tension. Results show also that OMA formation is enhanced when the Weber number approaches a value of 0.05.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".