Estimating the Usefulness of Chemical Dispersant to Treat Surface Spills of Oil Sands Products
Bibliographic record
Abstract
This study examines the use of chemical dispersant to treat an oil spill after the initial release. The natural and chemically enhanced dispersion of four oil products (dilbit, dilynbit, synbit and conventional crude) were investigated in a wave tank. Experiments were conducted in spring and summer to capture the impact of temperature, and the conditions in the tank were of breaking waves with a wave height of 0.4 m. The results showed that natural dispersion effectiveness (DE) was less than 10%. But the application of dispersant increased the DE by an order of magnitude with a statistically significant level (p < 0.05). Season (spring versus summer) had an effect on chemical DE of all oils, except for the conventional oil. Thus, the DE of dilbit products is highly dependent on the season/temperature. A model was fitted to the DE as a function of oil viscosity for the chemically dispersed oil, and the correlation was found to be very good. The model was then combined with a previous model compiled by the author predicting oil viscosity as a function of time, to produce a model that predicts the DE as function of time. Such a relation could be used for responders tackling oil spills.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".