Formation and Vertical Mixing of Oil Droplets Resulting from Oil Slick Under Breaking Waves—A Modeling Study
Bibliographic record
Abstract
Oil spilled at sea can be dispersed by a variety of natural processes, of which the influence of breaking waves is dominant. In this study, formation and the subsequent vertical mixing of oil droplets with respect to low and high wave energy quantities are investigated through a coupled modeling approach. Methods of computing the energy dissipation rate for the field waves were extended to support the modeling of oil droplet kinetics, including related vertical mixing and transport. The developed method was first examined with literature data including an agreement with results reported in Delvigne and Sweeney (1988) Delvigne, G. A. L. and Sweeney, C. E. 1988. Natural dispersion of oil. Oil and Chemical Pollution, 4: 281–310. [Crossref] , [Google Scholar]. Preliminary experimental validation was then conducted using a full-scale automated wave tank facility at the Centre for Offshore Oil and Gas Environmental Research (COOGER, Dartmouth Canada); consistency has been observed between experimental data and model predictions for the mean oil droplet diameter under breaking wave conditions for time intervals of 1, 10, 60, and 300 minutes after the spill. Outputs of this research will be used to improve existing oil spill modeling tools and to formulate effective oil spill countermeasures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".