Transport of Oil Droplets in the Upper Ocean: Impact of the Eddy Diffusivity
Bibliographic record
Abstract
Abstract The transport of oil droplets following a surface oil spill was investigated using a uniform vertical eddy diffusivity model and the K‐profile parameterization model, which assumes a maximum K value at 1/3 depth of the mixed layer. The initial droplet size distribution was obtained based on the Delvigne and Sweeney (1988, https://doi.org/10.1007/s13131‐013‐0364‐7 ) model. Using a uniform eddy diffusivity K ave , an exact analytical solution was used to produce the transient and steady state profile of the concentration of droplets of all sizes. It was found that the concentration at the surface is proportional to the droplet rise velocity and inversely proportional to K ave . Thus, small droplets (smaller than 100 μm) do not persist at the water surface. It was found that K‐profile parameterization produces smaller concentrations at the water surface than the uniform K model. The impact of waves was introduced into the K‐profile parameterization model through a roughness height, z o , that is comparable to the wave height. The investigation herein reveals that the Delvigne and Sweeney approach, commonly used in oil spill modeling, is not sufficient to predict the oil droplet size distribution, and that one needs to use a vertical eddy diffusivity to accurately predict the transport in the following hours and days. A new dimensionless formulation was provided to generalize the results, and showed that transport depends on three major parameters, the water friction speed, the mixed layer depth, and the droplet diameter.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".