Chemical dispersants within an environment plan and oil pollution emergency plan: practically applying a risk-based approach
Bibliographic record
Abstract
Chemical dispersant use can be a valid response strategy for marine oil pollution events. This peer-reviewed paper describes how a risk-based approach to planning can be applied practically using the core concepts of risk assessment. Comprehensive and systematic analysis is required in the environment plan to ensure that spill response strategies are in line with risk management requirements in the Offshore Petroleum and Greenhouse Gas Storage (Environmental) Regulations. In 2013, an APPEA working group identified the need for work to be undertaken that described this analysis for confirming the viability of chemical dispersant as a response strategy to support the mitigation of a marine oil pollution hazard. A literature review and interviews with oil and gas operators, regulatory agencies and industry service providers provided the basis for the process development. The result of this work is a process that is described in three parts: establish the context and risks; evaluate, demonstrate and define; and, implementation. Two flowcharts, and a description of each step, have been developed to assist planners in providing sufficient information to regulatory agencies assessing and accepting the use of dispersant operations. The information collected during the planning phase in Figure 1 is the basis for the net environmental benefit analysis that is undertaken in the activation phase of a response (Fig. 2). An outcome of this work is a process flow that oil spill planners can use to assess and plan spill response strategies that align with regulatory requirements.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".