Bibliographic record
Abstract
Natural dispersion occurs when fine droplets of oil are transferred into the water column by wave action or sea turbulence. Depending on oil conditions and the amount of sea energy available, natural dispersion can be insignificant or it can temporarily displace a portion of the oil. Current models predict the amount of oil entering the water column, but do not deal with their stability or how long these droplets stay in the water column. The most commonly-used model is by Delvigne, who carried out experiments in a flume. Delvigne measured the droplets entering the water column using a simplified procedure. These data were then converted to a model to predict the entry of droplets into the water column. Delvigne recommended procedures to calculate the resurfacing of the dispersed droplets but no models have implemented these. A review of the mathematics of this procedure show that the Delvigne model might be adjusted to be more unit consistent and to correctly incorporate oil viscosity. The other models used include the Audunson and Mackay models. These models are also reviewed. The Audunson model is simple and does not incorporate any inputs other than the wind speed. Further, the Audunson model predicts that most slicks will dissipate within a day or a few days. The Mackay model predicts little natural dispersion. Although the Mackay model incorporates a sea state function, the effect of this is not as great as in other models. Several issues have been noted about all natural dispersion models. These are: 1 In all cases natural dispersion models predicted the input of droplets into the water column and suggestions were made about predicting rise and resurfacing, but this important second part was never implemented by anyone,2 The natural dispersion predicted was measured as a temporary phenomenon - that is the instantaneous input of droplets into the water column. The persistence was not measured. The equation was designed to yield only the temporary transport in the water. Later workers assumed that the natural dispersion portion was permanently dispersed, and3 All models over-predict natural dispersion, especially in cases of low sea states.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".