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Record W2559527814 · doi:10.4043/27431-ms

A Joint Industry Programme to Improve Oil Spill Response in the Arctic

2016· article· en· W2559527814 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueArctic Technology Conference · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsArcticOil spillPetroleum industryWork (physics)Environmental scienceThe arcticEnvironmental resource managementEnvironmental planningBusinessOceanographyEngineeringEnvironmental protectionGeologyEnvironmental engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.019
GPT teacher head0.234
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it