Results and Commitments from the Zero Discharge Work on Produced Water Discharges on the Norwegian Continental Shelf
Bibliographic record
Abstract
Abstract The industry's goal to reach zero discharge is a response to the national goal established by the Norwegian government in White Paper No. 58 issued in 1996/97. The objective of the zero discharge strategy is to identify environmentally harmful chemical components of discharges so that cost effective measures can be developed and implemented to remove the risk of adverse impacts to the marine environment. The Environmental Impact Factor (EIF) is used as a tool for environmental risk assessment and further identification of the most efficient technology to reduce the risk. All companies operating in the Norwegian sector of the North Sea are currently working to reach the goal of zero discharges to sea within 2005. Plans for meeting the goal were reported to the Norwegian Pollution Control Authorities by June 1 2003. Several oil companies have already implemented new measures to reduce the risk of environmental harmful effects from discharges to sea. Based on the discharge reduction from implemented and planned measures, a prognosis for total reduction of discharges to sea in the Norwegian sector of the North Sea are presented. Results show that for fields on the Norwegian Continental Shelf, the total environmental risk (expressed as EIF) will be reduced by approximately 80% in 2006 compared to 2002. Results on volume reduction of produced water and dispersed oil to sea are also be presented, and compared with the new goals set by OSPAR.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".