Dynamic Resilience to Climate Change Caused Natural Disasters in Coastal Megacities Quantification Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objectives: The framework is designed to provide (i) for better understanding of factors contributing to urban resilience; and (ii) for comparison of climate change adaptation options. Methodology: Disasters occur at the intersection of hazards and vulnerabilities. As the climate changes, so do the patterns of climate hazards. Coastal megacities are faced with many challenges including (i) increased exposure to natural hazards such as hurricanes, typhoons, storm surges, sea-level rise and riverine flooding; (ii) pressures of increasing urbanization and population growth; and (iii) increased complexity of interacting subsystems. An original method for quantification of resilience is provided through spatial system dynamics simulation. The quantitative resilience framework combines economic, social, organizational, health and physical impacts of climate change caused natural disasters on coastal megacities. The developed measure defines resilience as a function of time and location in space. The framework is being implemented through the system dynamics model in an integrated computational environment. Conclusion: Data collection for the Coastal Megacity Resilience Simulator (CMRS) Research Article British Journal of Environment & Climate Change, 3(3): 378-401, 2013 379 model input and discussions with local decision makers are actively being pursued concurrent with the model development for the primary case study coastal city of Vancouver, British Columbia, Canada. Future work includes developing policy driven adaptation scenarios, resilience model simulations, transfer of the resilience model to local community and capacity building.
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.
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 | 0.001 | 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 | 0.001 |
| 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 it