A novel framework for urban flood resilience assessment at the urban agglomeration scale
Why this work is in the frame
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Bibliographic record
Abstract
With global climate change and continuous urbanization exacerbating floods, urban flood resilience (UFR) has become a key to cope with floods. However, few studies target frameworks of UFR assessment at the urban agglomeration scale over a longer time span. This study, taking the Yangtze River Delta urban agglomeration as a case study, developed an evaluation framework to detail the building of a final evaluation index system of UFR, to analyze UFR’s driving factors and spatiotemporal features based on the SSA-PP-KL-TOPSIS (projection pursuit based on sparrow search algorithm-Kullback-Leibler-technique for order of preference by similarity to ideal solution) model. From the perspectives of comparing numerical values and spatial distribution results, the evaluation indicators and method proposed in this article perform better. The case results showed that UFR displayed an overall growth trend and significant spatial heterogeneities. The economic, social, and infrastructure resilience showed a similar growth trend, while the environmental resilience demonstrated a decreasing trend. Environmental resilience has become a weak link in improving resilience. Higher resilience levels were concentrated in the central metropolis, provincial capitals, and industrial cities. The findings could be of use to researchers and practitioners, and the framework presented would be of reference to other flood-stricken areas.
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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