Urban Blue Spaces
Bibliographic record
Abstract
This book presents an evidence-based approach to landscape planning and design for urban blue spaces that maximises the benefits to human health and well-being while minimising the risks. Based on applied research and evidence from primary and secondary data sources stemming from the EU-funded BlueHealth project, the book presents nature-based solutions to promote sustainable and resilient cities.Numerous cities around the world are located alongside bodies of water in the form of coastlines, lakes, rivers and canals, but the relationship between city inhabitants and these water sources has often been ambivalent. In many cities, water has been polluted, engineered or ignored completely. But, due to an increasing awareness of the strong connections between city, people, nature and water and health, this paradigm is shifting.The international editorial team, consisting of researchers and professionals across several disciplines, leads the reader through theoretical aspects, evidence, illustrated case studies, risk assessment and a series of validated tools to aid planning and design before finishing with overarching planning and design principles for a range of blue-space types. Over 200 full-colour illustrations accompany the case-study examples from geographic locations all over the world, including Portugal, the United Kingdom, China, Canada, the US, South Korea, Singapore, Norway and Estonia. With green and blue infrastructure now at the forefront of current policies and trends to promote healthy, sustainable cities, Urban Blue Spaces is a must-have for professionals and students in landscape planning, urban design and environmental design. Open Access for the book was funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 666773
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.136 | 0.035 |
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".