Effectiveness of chemical dispersants used in broken ice conditions
Bibliographic record
Abstract
ABSTRACT (no. 2017-095) In 2012, the International Association of Oil and Gas Producers initiated “The Arctic Oil Spill Response Technology Joint Industry Program (JIP)” with nine companies funding projects in a wide range of technical areas. This paper summarizes results from the project “Dispersant Testing under Realistic Conditions”, a collaboration between SINTEF (Norway) and SL Ross (Canada). The objective of the research was to build on current knowledge to increase understanding of the effect of oil type, degree of weathering, and environmental conditions on dispersant effectiveness in ice-covered waters. SINTEF and SL Ross performed approximately 70 tests using two identical recirculating flumes with variable parameters such as oil types, dispersant type, mixing energy, ice coverage, and water salinity. The oils were weathered in the flumes for 6 or 18 hours under simulated winds, waves, and cold temperatures to represent weathering that might occur at sea prior to dispersant application. The dispersed oil was exposed to various mixing energies, starting with low energy waves, followed by somewhat higher energy waves, and finally by applying propeller wash. Four crude oils were studied and the dispersant efficiency of three commercial oil spill dispersants were evaluated for the tested oils. Other test parameters were ice coverage (50% and 80%) and water salinity (35, 15, and 5 ppt). Dispersant effectiveness as a function of the different test variables were estimated using results from the flume-based experiments. As expected, shorter weathering times resulted in an increase in dispersant efficiency. The dispersant effectiveness varied with both oil type and dispersant type applied, and the effectiveness increased when higher mixing energy conditions were used. Varying the ice cover did not influence the results significantly, but water salinity did, with the lowest dispersant efficiencies found at 5 ppt salinity. The conclusions are based on the findings from testing performed under the controlled conditions and may not be directly transferable to all conditions that could be encountered in the Arctic. However, this study shows that dispersants can be considered as a response option for spills in ice, but effectiveness needs to be validated in the field during an actual event.
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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) | 0.000 | 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".