Editorial: Building flood resilience under climate change
Bibliographic record
Abstract
Millions of people in addition to the danger of flooding live in severe poverty and are directly in danger of flooding because of their disadvantages. Approximately one-third of global economic losses are attributed to the catastrophic effects of flood occurrences. Flood risk assessment is especially crucial since flood threats are broad, expensive, and disproportionately affect economically vulnerable people (Tascón-González et al., 2020). One of the most difficult issues to be solved is how to make our societies more resilient to flooding in the face of climate change. To tackle this inquiry, a paradigm change from reactive crisis management to proactive evaluation and mitigation of flooding risk is necessary (Wenger, 2016). Documenting the most recent advancements in flood resilience considering climate change is the aim of this research topic. We gathered five relevant articles for this research topic.• The paper titled "The Stackelberg Game Model of Cross-Border River Flood Control" by Wang et al. uses cooperative governance among the nations in the Lancang-Mekong River Basin (LMRB) as an example. The paper demonstrates when flood control in the upstream region has a larger influence on the downstream region, flood control in the downstream region progressively grew and flood control in the upstream region gradually diminished with an increase in flood control compensation.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.020 | 0.014 |
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".