From Net Environmental Benefit Analysis to Spill Impact Mitigation Assessment SIMA
Bibliographic record
Abstract
Abstract A key objective for any oil spill response effort is to minimize the impacts to ecological, socio-economic and cultural resources at risk. To that end, the contingency planners and incident managers have traditionally utilized a formal or informal Net Environmental Benefit Analysis (NEBA) for selecting the most appropriate response option(s) to minimize spill impacts and promote recovery. The processes used to conduct a NEBA have varied considerably between industry operators, though the outcomes in terms of response strategy development have been similar. This variation in NEBA approaches can lead to challenges with communicating the underlying basis of response strategies to stakeholders. The oil industry published updated guidance in 2015 to explain the general principles of the NEBA process and facilitate stakeholder involvement. However, with industry's increasing reliance on NEBA to enhance the transparency of response strategy development, a consistent methodology for conducting formal NEBAs was required. In response to the above issue key industry associations collaborated on developing a qualitative NEBA methodology that was renamed Spill Impact Mitigation Assessment (SIMA). This methodology is designed to give a consistent approach to larger or higher consequence oil spill scenarios, where multiple spill response options are being considered and a formal SIMA/NEBA is warranted. Several industry spill response specialists and an independent expert participated in this project, resulting in the development of ‘Guidance on Implementing Spill Impact Mitigation Assessment’. The SIMA methodology is described, representing a tool that identifies the response option(s) that will best mitigate the overall impacts of an oil spill.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
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".