Research to Improve Oil Spill Response in the Arctic - A Joint Industry Programme
Bibliographic record
Abstract
Abstract For more than 50 years, the oil and gas industry has funded and conducted research to improve oil spill response technologies and methodologies with industry, government, academia, and stakeholders jointly involved. This research has included hundreds of studies, laboratory and basin experiments and field trials, specifically in the United States, Canada and Scandinavia. Recent examples include the SINTEF Oil in Ice JIP (2006-2009) http://www.sintef.no/Projectweb/JIP-Oil-In-Ice/Publications and research conducted at Ohmsett - The National Oil Spill Response Research and Renewable Energy Test Facility www.ohmsett.com/activities.html. This sustained and frequently collaborative effort is not commonly known and recognized by those outside the field of oil spill response. To build on this existing research and continue improving the technologies and methodologies for arctic oil spill response, ten international oil and gas companies (BP, Chevron, ConocoPhillips, Eni, ExxonMobil, Gazprom-neft, North Caspian Operating Company (NCOC), Shell, Statoil, and Total) are working collaboratively in the Arctic Oil Spill Response Technology - Joint Industry Programme (JIP). The goal is to advance arctic oil spill response strategies and equipment as well as to increase understanding of potential impacts of oil on the marine environment. The programme is coordinated by an Executive Steering Committee comprising representatives from each company. The International Association of Oil and Gas Producers (OGP) is providing project management expertise and the world's foremost experts on oil spill response, development, and operations from across industry, academia, and independent scientific institutions are being engaged to perform the scientific research. The JIP has completed phase one that included technical assessments and state of knowledge reviews in the following six areas: dispersants, environmental effects, trajectory modelling, remote sensing, mechanical recovery, and in situ burning (ISB). Nine research reports are available on the JIP website (www.arcticresponsetechnology.org) that identified and summarised the state-of- knowledge and regulatory status for using dispersants, remote sensing and ISB in the Arctic. Phase two activities are now underway that include laboratory, small and medium scale tank tests, and field research. Eleven projects are in progress ranging from dispersant effectiveness testing; modelling the fate of dispersed oil in ice; assessing the environmental effects of an arctic oil spill; advancing oil spill modelling trajectory capabilities in ice; extending the capability to detect and map oil in darkness, low visibility, in and under ice; improving efficiency of mechanical recovery equipment in ice; chemical herder fate and effects; and expanding the ‘window of opportunity’ for ISB response operations. This paper presents recent JIP progress.
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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 | 0.063 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".