Nature-Based Solutions for Urban Sustainability
Bibliographic record
Abstract
Abstract An accessible ePub edition is available here Nature-Based Solutions for Urban Sustainability provides comprehensive insights on existing technologies and up-to-date advances in the field of water, wastewater and waste treatment using nature-based approaches and systems. This book highlights: Process fundamentals of nature-based solutions, including hydrodynamics, media, bacteria/media interactions and phytoremediation for pollution control, resource recovery and energy generation.Critical insights on the status, major challenges and modern engineering solutions in nature-based solutions for the treatment of rainwater, storm water, wastewater and solid waste.Advanced methods for valorisation using nature-based solutions through integration with other technologies, such as composting, anaerobic digestion and bioelectrochemical systems.Up-to-date information on modern approaches for deriving value-added operation, by combining nature-based solutions with agricultural practices such as fish farming or protein production.Case studies of nature-based solutions from countries in transition including Thailand, Vietnam, Indonesia and Philippines. This reference textbook is recommended reading for both undergraduate and graduate students pursuing degrees in environmental sciences, technologies, or engineering. It is equally useful for a broader audience including researchers, engineers, and policy makers interested in the field of nature-based solutions for urban sustainability. It is also tailored to be used as an advanced manual for practitioners and consultancies working in the field of diffuse pollution and climate change mitigation. ISBN: 9781789065008 (paperback) ISBN: 9781789065015 (eBook) ISBN: 9781789065022 (ePub)
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.141 | 0.045 |
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".