Forecasting Photo-Dissolution for Future Oil Spills at Sea: Effects of Oil Properties and Composition
Bibliographic record
Abstract
), resulting in faster rates for lighter crudes. However, photo-dissolution rates and importance to oil mass balance varied as a function of both reactivity and properties that govern slick thickness and light absorbance. Thicker slicks (∼1 mm) of light and heavy crudes produced more DOC by photo-dissolution compared to thin slicks due to higher rates of light absorbance. However, the mass lost from thin slicks (∼1 μm) was quantitatively relevant for calculations of oil mass balance, with a modeled ∼5% loss for a simplified, hypothetical spill after 1 day of sunlight exposure. The ULSFO was unusual in its exceptionally low photo-reactivity, suggesting distinct fates for this high-spill-risk product. The results show that photo-dissolution is a relevant fate process for a wide range of oil products and that it is controlled by oil properties and composition, making possible predictions of oil fate and effects for future spills at sea.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".