Impacts of Frazil Ice on the Effectiveness of Oil Dispersion and Migration of Dispersed Oil
Bibliographic record
Abstract
Oil spills in the Arctic have drawn dramatic attention in recent years. Frazil ice, as the essential formation of sea ice, may affect the effectiveness of dispersants during oil spill response and the associated behaviors of dispersed oil. However, these impacts remain poorly understood, limiting the appropriate usage of dispersants in ice-covered regions. Herein this work explored the effects of frazil ice on the dispersion effectiveness of two dispersants (Corexit 9500A and hydrolyzed shrimp waste) and the migration of dispersed oil within frazil ice. We discovered that frazil ice inhibited dispersion effectiveness by attenuating water velocity. Permeable frazil ice encapsulated 11-30% of dispersed oil, implying a lower oil bioavailability. We thus proposed and verified a microscopic mechanism to unravel the migration of dispersed oil toward permeable constrictions in frazil ice. We predicted the concentration of dispersed oil encapsulated in frazil ice using bed filtration theory and verified the prediction through experiments. Furthermore, the presence of frazil ice can lead to the breakup and coalescence of dispersed oil. Overall, our findings would facilitate the appropriate planning and decision-making of dispersant-based oil spill response and a better understanding of the fate of dispersed oil in the frazil ice-infested ocean.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".