The EU Horizon 2020 project GRACE: integrated oil spill response actions and environmental effects
Bibliographic record
Abstract
This article introduces the EU Horizon 2020 research project GRACE (Integrated oil spill response actions and environmental effects), which focuses on a holistic approach towards investigating and understanding the hazardous impact of oil spills and the environmental impacts and benefits of a suite of marine oil spill response technologies in the cold climate and ice-infested areas of the North Atlantic and the Baltic Sea. The response methods considered include mechanical collection in water and below ice, in situ burning, use of chemical dispersants, natural biodegradation, and combinations of these. The impacts of naturally and chemically dispersed oil, residues resulting from in situ burning, and non-collected oil on fish, invertebrates (e.g. mussels, crustaceans) and macro-algae are assessed by using highly sensitive biomarker methods, and specific methods for the rapid detection of the effects of oil pollution on biota are developed. By observing, monitoring and predicting oil movements in the sea through the use of novel online sensors on vessels, fixed platforms including gliders and the so-called SmartBuoys together with real-time data transfer into operational systems that help to improve the information on the location of the oil spill, situational awareness of oil spill response can be improved. Methods and findings of the project are integrated into a strategic net environmental benefit analysis tool (environment and oil spill response, EOS) for oil spill response strategy decision making in cold climates and ice-infested areas.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".