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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".