A holistic approach for assessing impact of extreme weather on critical infrastructure
Bibliographic record
Abstract
Urban infrastructures are essential to the health, safety, security and economic well-being of citizens and organisations.Therefore, the managers of critical infrastructures (CI) and infrastructure systems in urban areas need to be constantly aware of and prepared for to any man-made and natural disasters.In this paper, we propose a structured approach to assess extreme weather impacts on CI and discuss how resilience and risk tolerance of critical infrastructure can be enhanced.The approach is aimed at supporting CI owners' and managers' decision-making on a strategic level.It follows a process flow from hazard and CI identification, vulnerability analysis, potential damage estimation, loss assessment to identification and assessment of measures.The approach incorporates many elements, phases and methods from hazard assessment, vulnerability assessment, risk assessment and cost-benefit analysis (CBA), and combines and incorporates them into one aggregated structure, thus providing a holistic view to risk management and CI protection.The proposed approach is flexible in the sense that it encompasses not only a rigorous quantitative assessment, but also allows for a semi-quantitative or qualitative assessment.In addition, the approach enhances transparency of decision making and contributes to more comprehensive use of available information.The paper is based on research carried out in the INTACT and HARMONISE projects, which are co-funded by the European Union under the 7th Framework Programme.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".