Vulnerability and Recovery Co-Analysis to Enhance Resilience of Ports Impacted By Extreme Weather Events - Preliminary Results from EU Project Safari
Bibliographic record
Abstract
Ports play a critical role in global commerce, acting as vital nodes in the supply chain for goods and services.However, their strategic coastal locations make them particularly vulnerable to extreme weather events, intensified nowadays by climate change.In Europe, where ports are integral to economic stability and regional connectivity, the need for robust vulnerability analysis and resilience planning has become a pressing concern.This paper explores the first steps in the process of establishing the practical application of a combined framework for assessing port vulnerabilities and developing recovery plans to mitigate the impacts of extreme weather events on port operations.The study focuses on the European ports considered in the research project Safe and Climate Resilient Ports (SAFARI).The historical weather analysis shows that the SAFARI project ports are subject to an increasing risk of flooding and heatwaves.The definition of port functions, associated stakeholders and infrastructure together with their vulnerability to these events is conducted as a starting point to develop port recovery plans as part of port resilience capabilities
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".