A risk management framework for maritime Pollution Preparedness and Response: Concepts, processes and tools
Bibliographic record
Abstract
Several risk management frameworks have been introduced in the literature for maritime Pollution Preparedness and Response (PPR). However, in light of the actual needs of the competent authorities, there is still a lack of framework that is established on a sound risk conceptual basis, addresses the different risk management decision-making contexts of organizations, and provides tools for various risk management questions of this field. To alleviate the limits of existing approaches, this paper introduces a new risk management framework for this purpose, which was developed in cooperation with the competent authorities and other maritime experts. The framework adopts the risk-informed decision-making strategy and includes three aligned components. The first component provides a unified theoretical risk concept to the framework through an interpretation of the Society for Risk Analysis risk approach. The second consists of four ISO 31000:2018 standard based processes focused on different risk management decision-making contexts of the PPR organizations. The third comprises a set of practical risk assessment tools to generate the needed information. A case study provides an example of the functionality of this framework with integrated data from the northern Baltic Sea. To conclude, a risk concept is provided for the PPR authorities and their stakeholders as well as processes for managing the risk and tools for its assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".