THEORETICAL FOUNDATION FOR PREDICTING DISPERSION EFFECTIVENESS DUE TO WAVES
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Bibliographic record
Abstract
ABSTRACT The studies of dispersion of oil in wave tanks have reached their maturity in terms of analytical techniques for measuring and quantifying dispersion. However, there does not seem to be a theoretical framework for predicting or even interpreting the results based on the physics of the problem. One of the reasons is that the oil breakup studies were based on chemical reactors where the energy input is constant with time whereas the energy input to droplets varies with time under waves. For this reason, we present a holistic approach that accounts for the duration over which the oil is subjected to various intensities along with a droplet kinetics model that uses a variable energy dissipation rate function. A salient advantage of the droplet model is that it accounts for the effects of scale of problem, because it has been observed that large systems produce smaller droplets than smaller systems with the same average kinetic energy dissipation rate. We illustrate the usage of the model using simulated wave data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it