Quantifying the effects of nature-based solutions in reducing risks from hydrometeorological hazards: Examples from Europe
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
The combination of climate change and social and ecological factors will increase risks societies face from hydrometeorological hazards (HMH). Reducing these risks is typically achieved through the deployment of engineered (or grey) infrastructure but increasingly, nature-based solutions (NBS) are being considered. Most risk assessment frameworks do not allow capturing well the role NBS can play in addressing all components of risk, i.e., the hazard characteristics and the exposure and vulnerability of social-ecological systems. Recently, the Vulnerability and Risk assessment framework developed to allow the assessment of risks in the context of NBS implementation (VR-NBS framework) was proposed. Here, we carry out the first implementation of this framework using five case study areas in Europe which are exposed to various HMH. Our results show that we can demonstrate the effect NBS have in terms of risk reduction and that this can be achieved by using a flexible library of indicators that allows to capture the specificities of each case study hazard, social and ecological circumstances. The approach appears to be more effective for larger case study areas, but further testing is required in a broader variety of contexts.
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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 | 0.000 |
| 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