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
In the face of escalating climate challenges, "SOS Climate Waterfront" emerges as a compilation of strategies that bridges the gap between climate change challenges and urban waterfront planning. Through a collaborative effort supported by the Horizon 2020 Marie Skłodowska-Curie grant, this book brings together thoughts and findings from experts across fields like architecture, urban planning, and environmental science. The book explores innovative ways to make cities along waterfront more resilient against climate threats. It showcases projects and strategies that combine the old with the new, ensuring that cities can withstand future climate impacts while maintaining their cultural essence and boosting community life. It aims to spark a transformation in how waterfront cities cope with climate change. As sea levels rise and flooding becomes more frequent, it's crucial for urban planners, architects, and policymakers to rethink how cities can adapt. This book fills the crucial need for a modern guide that integrates cultural heritage with sustainable urban development, presenting a unified approach to climate adaptation. "SOS Climate Waterfront" tackles the pressing issue of enhancing urban resilience along waterfronts. It guides readers through understanding the risks, opportunities, and innovative strategies necessary for developing sustainable cities that are ready for future climate conditions. This book is designed to be both practical and inspiring, offering a roadmap for integrating environmental care with urban development, ensuring cities not only survive but thrive in the face of climate challenges. It serves as a tool for those involved in city planning and community building, enriching their projects with forward-thinking approaches and sustainable practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.031 | 0.002 |
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