End-user and Stakeholder Views on Selected Risk Assessment Tools for Marine Oil Spill Preparedness and Response, Including Future Research and Development Needs
Bibliographic record
Abstract
Risks in the maritime domain have various sources, of which the transportation of oil and other noxious products is one of key concern to industry and public stakeholders.Operational or accidental releases of oil or other pollutants from ships or offshore facilities into the marine environment can have disastrous effects on the marine ecosystems, while also leading to very significant economical losses.Therefore, national states have implemented various mechanisms for preventing and responding to pollution in the maritime domain, with activities which are often embedded in regional cooperation frameworks clustered around certain sea areas.To support collaborative, harmonized, and risk-informed oil spill Pollution Preparedness and Response (PPR) planning for response authorities, the Baltic Marine Environment Protection Commission (HELCOM), together with its research partners, and with extensive end-user and stakeholder inputs, have developed the OpenRisk Toolbox.This toolbox includes several risk assessment tools and techniques, which can assist in providing answers to a range of PPR risk management questions in a range of organizational contexts.To better understand and ensure the applicability and usefulness of the OpenRisk Toolbox, a workshop was organized where some of these tools were tested.Selected end user and stakeholder views on the perceived usefulness of the tools were collected and analyzed.Another workshop focused on further development needs to implement the tools in organizational practices.This paper first presents the OpenRisk Toolbox, then describes the settings of the workshops.Finally, a summary of the end-user and stakeholder views on the tested tools, and on future development needs, is given.http://www.transnav.eu
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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 | 0.065 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".