A Joint Industry Research Programme to Improve Oil Spill Response Technologies and Methodologies for Use in the Arctic Offshore Environment
Bibliographic record
Abstract
Abstract For more than 50 years, the oil and gas industry has jointly funded and conducted research with industry, government, academia, and stakeholders to advance and improve arctic oil spill response technologies and methodologies and understand the potential impacts on the marine environment. This sustained and frequently collaborative effort is not commonly known and recognised by those outside the field of oil spill response. This research has included hundreds of studies, laboratory and basin experiments and field trials, specifically in the United States, Canada and Scandinavia (Potter et al 2012). Recent examples include the SINTEF Oil in Ice JIP (2006–2009) (Sørstrøm et al 2010) and research conducted at Ohmsett - The National Oil Spill Response Research and Renewable Energy Test Facility. To continue to build on this existing research, nine international oil and gas companies (BP, Chevron, ConocoPhillips, Eni, ExxonMobil, North Caspian Operating Company (NCOC), Shell, Statoil, and Total) are working collaboratively in the Arctic Oil Spill Response Technology - Joint Industry Programme (JIP). The purpose of the programme, the largest pan-industry programme dedicated to this area of research, is to further enhance industry knowledge and capabilities in the area of arctic oil spill response. 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 programme has completed phase one, which 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). Twelve research reports that identify and summarise the state-of- knowledge and regulatory status for using dispersants, in situ burn, mechanical recovery, and remote sensing in the Arctic are available on the JIP website (www.arcticresponsetechnology.org). Phase two activities actively underway include modelling studies, laboratory and meso-scale basin testing and field experiments. Special emphasis is laid on gaining knowledge for the development of an Arctic Net Environmental Benefit Analysis (NEBA) tool to support operational decision-making for oil spill response strategy in the Arctic.
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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 | 0.073 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".