In Situ Oil Spill Countermeasures in Ice-Infested Waters: A Modeling Study of the Fate/Behaviours of Spilled Oil
Bibliographic record
Abstract
ABSTRACT The expansion of offshore oil and gas and marine transport activities in the Arctic have raised the level of risk for an oil spill to occur in the Arctic region. Existing technologies for oil spill cleanup in ice-covered conditions are limited and there is a need for improved oil spill countermeasures for use under Arctic conditions. A recent field study has assessed a proposed oil spill response technique in ice-infested waters based on the application of fine minerals in a slurry with mixing by propeller-wash to promote the formation of oil-mineral aggregates (OMA). While it was verified in the experimental study that the dispersion was enhanced and mineral fine additions promoted habitat recovery by enhancing both the rate and extent of oil biodegradation, limited monitoring data provide little insights on the fate of dispersed oil after the response. To help understand the oil transport process following mineral treatment in ice-covered conditions, mathematical modeling was used in this study to simulate the transport of OMA and calculate the mass balances of the spilled oil. To study the effects of ice and minerals on the fate and transport, the result was compared with scenarios without ice and without the addition of mineral fines. The results show general agreement between the modeling results and field observations, and further confirm the effectiveness and potential for using mineral treatment as a new oil spill counter-measure technology. This technique offers several operational advantages for use under Arctic conditions, including reduced number of personnel required for its application, lack of need for waste disposal sites, and cost effectiveness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".