Net Environmental Benefit Analysis Embedded Action Plan Development
Bibliographic record
Abstract
Abstract During a response to an oil spill the responsible party needs to develop Incident Action Plans that aim to minimise the environmental and socio-economic effects of the incident on the surrounding area. It is widely accepted that Net Environmental Benefit Analysis (NEBA) and the Spill Impact Mitigation Assessment (SIMA) methodology should be considered before, during and after any spill response. However, during the initial response phase, actions are typically reactive. Decisions may therefore be based on the needs of the response, media or the local inhabitants rather than the long-term benefits to the affected area. But how can we confirm that when the strategies and tactics are developed NEBA/SIMA is taken into account? To ensure that all response options chosen have considered NEBA/SIMA, especially during the initial stages of a response, it should be embedded into the action plan development process. This will also capture the decision-making process for the strategy and tactical plan development as evidence. This paper explores where an Incident Management System (IMS) could be amended to ensure that NEBA/SIMA is integrated into decision making. This should guarantee that NEBA/SIMA is always considered in determining the correct response operations that capitalize on the net environmental benefits for the response. The process will follow the IPIECA/IOPG good practice guidelines for incident management implementation of IMS and contest the existing formal processes found in Incident Command System (ICS).
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".