Coastal Natural and Nature-Based Features: International Guidelines for Flood Risk Management
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
Natural and nature-based features (NNBF) have been used for more than 100 years as coastal protection infrastructure (e.g., beach nourishment projects). The application of NNBF has grown steadily in recent years with the goal of realizing both coastal engineering and environment and social co-benefits through projects that have the potential to adapt to the changing climate. Technical advancements in support of NNBF are increasingly the subject of peer-reviewed literature, and guidance has been published by numerous organizations to inform technical practice for specific types of nature-based solutions. The International Guidelines on Natural and Nature-Based Features for Flood Risk Management was recently published to provide a comprehensive guide that draws directly on the growing body of knowledge and practitioner experience from around the world to inform the process of conceptualizing, planning, designing, engineering, and operating NNBF. These Guidelines focus on the role of nature-based solutions and natural infrastructure (beaches, dunes, wetlands and plant systems, islands, reefs) as a part of coastal and riverine flood risk management. In addition to describing each of the NNBF types, their use, design, implementation, and maintenance, the guidelines describe general principles for employing NNBF, stakeholder engagement, monitoring, costs and benefits, and adaptive management. An overall systems approach is taken to planning and implementation of NNBF. The guidelines were developed to support decision-makers, project managers, and practitioners in conceptualizing, planning, designing, engineering, implementing, and maintaining sustainable systems for nature-based flood risk management. This paper summarizes key concepts and highlights challenges and areas of future research.
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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 | 0.000 | 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 | 0.001 |
| 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