Enhancing urban flood resilience: A coupling coordinated evaluation and geographical factor analysis under SES-PSR 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
Urban flooding has emerged as a significant urban issue in cities worldwide, with China being particularly affected. To effectively manage and mitigate urban floods, a holistic examination of the interaction between urban subsystems is required to improve urban flood resilience. However, the interactions and mechanisms between urban subsystems under flood disaster haven't been addressed adequately in previous studies. Therefore, this paper established a conceptual framework for illustrating the interactions between urban natural-ecological and social-economic subsystem considering urban pressure, state, and response within flood cycle. The objective is to investigate the coupling coordination degree (CCD) between these subsystems and identify the driving factors with a geographical detector model, and the cities in Yangtze River Delta are selected as an empirical example. The findings reveal an overall upward trend towards coordination for the whole area with notable variability among the cities. The resilience of the state dimension emerges as a crucial aspect in determining the CCD of the urban flood resilience of the area. Key driving factors for the coordinated development of urban flood resilience are identified as air pollution, global warming, technological innovation, governance power, financial strength, and urbanization. Based on the findings and the interactions among the driving factors, this paper presents potential implications that can serve as effective guidance and offer insights for policymakers, planners, and researchers in their efforts to enhance urban flood resilience for sustainable development in the future.
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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 | 0.001 |
| 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) | 0.001 | 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