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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.075 | 0.018 |
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".