A Joint Industry Programme to Improve Oil Spill Response in the Arctic
Bibliographic record
Abstract
Abstract Results from hundreds of studies, laboratory and basin experiments and field trials conducted worldwide over the past 50 years, in particular in the United States, Canada and Scandinavia, show that the industry has a wide range of viable technologies, beyond mechanical recovery, for oil spill response in the presence of ice in open water. To continue to build on this existing research and improve the technologies and methodologies for Arctic oil spill response, nine international oil and gas companies (BP, Chevron, ConocoPhillips, Eni, ExxonMobil, North Caspian Operating Company, Shell, Statoil, and Total) are working collaboratively in the Arctic Oil Spill Response Technology - Joint Industry Programme (JIP). The JIP has brought together the world’s foremost experts on oil spill response research, development, and operations from across industry, academia, and independent research centres to undertake the technical work and scientific studies. The core areas of research are: dispersants, environmental effects, trajectory modelling, remote sensing, mechanical recovery, and in situ burning (ISB) in Arctic and ice-prone regions. Significant work is committed to developing a robust information database that will support the use of Net Environmental Benefit Analysis for response decision-making and environmental impact assessments related to the Arctic environment. Phase one of the JIP is complete and seventeen research reports dedicated to literature and state-of-theart reviews are available on the JIP website (www.arcticresponsetechnology.org). This initial phase identified specifically targeted research projects to improve industry capabilities and coordination in the area of Arctic oil spill response. Phase two activities actively underway include dispersant effectiveness testing; modelling the fate of dispersed oil in ice; assessing the environmental effects of an Arctic oil spill; advancing oil spill trajectory modelling capabilities in ice; extending the capability to detect and map oil in darkness, low visibility, in and under ice; and expanding the ‘window of opportunity’ for ISB response operations. The JIP is committed to sharing information with the public on the progress and results of its projects with the objective of improving Arctic spill response capabilities.
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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 | 0.027 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".