Use of the DREAM Model for Control and Prediction of Concentrations and Environmental Risks Associated with Regular Discharges to Sea: Experiences and Challenges.
Bibliographic record
Abstract
Abstract A numerical model has been developed and applied to serve the offshore industry to predict and report environmental risks associated with offshore discharges. The development has taken place over a time period of about 20 years, and is fully financed by the offshore operators. The model is fully four-dimensional (time and 3D space variations included) and covers the water column and sediment compartments. The model was first developed and applied to produced water discharges. Later, drilling discharges and coastal discharges (terminal releases) were included. At present, the "DREAM Charter" project focuses on revisions of the risk approach according to recent literature, validation of the numerical model by comparing with laboratory and field data, and also addresses the uncertainties in the predictions. Present participants are BP, ConocoPhillips, Det Norske, ENI, Statoil and Total. The model tool has been applied to predict and report environmental risks associated with discharges to sea (mostly produced water and drilling discharges). A parameter, EIF (Environmental Impact Factor), has been developed and used as a measure for size of potential environmental impact. This parameter has also been used as a basis for selection of chemicals used, and to give priority to the most cost efficient measures to reduce environmental impact. The model tool makes it possible to select the best option to reduce environmental impact in a cost efficient manner, weighting reduction of the size of the EIF against costs. The introduction of new OSPAR regulations (OSPAR, 2012) allows for an alternative approach (Whole Effluent Approach) which is discussed as well.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".