Dynamic risk framework for cascading compounding climate-geological hazards: A perspective on coastal communities in subduction zones
Bibliographic record
Abstract
Integrated disaster resilience approaches are paramount for enhancing risk management of coastal communities in subduction zones under climate-geological risks. Cascading compounding climate-geological risks are dynamic, interact, and evolve with time. Also, classical convolution of hazard, vulnerability, and exposure does not allow easy implementation of causalities and dependencies that are critical in modelling impact chains due to cascading compounding multi-hazards. A new dynamic multi-hazard catastrophe modelling framework is proposed to fill important research gaps in the temporal evolution of climate-geological risks. Systemic modelling is a key tool where time-dependent and interacting risks are explicitly modelled, and multiple risk metrics can be computed. Furthermore, a possible new entropy-based approach for harmonising risk metrics is presented. Such a harmonisation can facilitate the decision-making process for disaster risk reduction. Finally, a way forward for future implementation of the proposed methodologies is discussed with a focus on the need for the development of new numerical and analytical tools.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".