Hot and Cold: Photochemical Weathering Mediates Oil Properties and Fate Differently Depending on Seawater Temperature
Bibliographic record
Abstract
Photochemical weathering transforms petroleum oil and changes its bulk physical properties, as well as its partitioning into seawater. This transformation process is likely to occur in a cold water marine oil spill, but little is known about the behavior of photochemically weathered oil in cold water. We quantified the effect of photochemical weathering on oil properties and partitioning across temperatures. Compared to weathering in the dark, photochemical weathering increases oil viscosity and water-soluble content, decreases oil-seawater interfacial tension, and slightly increases density. Many of these photochemical changes are much larger than changes caused by evaporative weathering. Further, the viscosity and water-soluble content of photochemically weathered oil are more temperature-sensitive compared to evaporatively weathered oil, which changes the importance of key fate processes in warm versus cold environments. Compared to at 30 °C, photochemically weathered oil at 5 °C would have a 16× higher viscosity and a 7× lower water-soluble content, resulting in lower entrainment and dissolution. Collectively, the physical properties and thus fate of photochemically weathered oil in a cold water spill may be substantially different from those in a warm water spill. These differences could affect the choice of oil spill response options in cold, high-light environments.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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 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".