Dispersion of Oil Droplets in Rivers
Bibliographic record
Abstract
The dispersion of oil droplets in rivers was numerically investigated for uniform flow in a hypothetical wide river with a depth of 3.0 m. The river hydrodynamics profile was used in conjunction with the VDROP model to produce the oil droplet size distribution (DSD), whereas the NEMO3D model was used to track the movement of the oil droplets. Results suggest that the gradient of eddy diffusivity significantly affected the upward-normal (i.e., quasi-vertical) transport of the droplets, and caused them to mix rapidly through the depth. We also found that an increase in buoyancy resulted in a decrease in the streamwise variance and spreading coefficient. Oil droplets broke up due to the relatively large energy dissipation rates in the river at approximately 1.0 m below the surface and deeper. The droplet breakup varies DSD in the river water column, which may subsequently affect other chemo-physical processes (e.g., oil-particle aggregation). The breakup efficiency is affected by a system-dependent parameter Kb, which reflects the uncertainty of a system. The steady-state DSD was bimodal for the case Kb=0.05, whereas it was unimodal for larger Kb values (i.e., Kb=1.0 and 0.25), respectively. More small-sized droplets were generated and persisted in the deep-water column with larger Kb values. The droplet breakup also enhanced the streamwise spreading of the plume. The effect of droplet entrainment on oil dispersion was studied by assuming constant entrainment probabilities of surface oil droplets. The oil DSD varied with different droplet entrainment probabilities, and the number of oil droplets generated in the water column decreased significantly with a decrease in the entrainment probability of the oil droplets.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".