Nature-based evolution: Traditional ecological practices in the application of blue-green infrastructure for climate resilience
Bibliographic record
Abstract
In cities, climate hazards continue to escalate due to the impacts of a changing climate. This poses growing risks to both ecosystems and human populations. Urban resilience depends on the capacity of cities to respond to climate-related pressures such as rising temperatures and extreme flooding. To address these challenges, urban planners must adopt strategies that reduce climate hazards while improving the well-being of residents and urban ecosystems. Traditional ecological practices and historic blue-green infrastructure (BGI) can provide climate-proof strategies to increase urban climate resilience. Throughout history, humans have learned to understand, interpret, interact and adapt to their biophysical environments. This has generated a body of knowledge and traditional wisdom about nature-based solutions to manage environmental change. Colonization, industrialization, and urbanization have transformed spatial relationships, resulting in fragmented blue-green networks within the landscape. A study of traditional BGI practices is presented that explores and documents common forms of historical BGI and traditional ecological practices across global contexts, examining their relevance in nature-based decision-making for sustainable and climate-proof cities. As part of this study, mapping of a common form of historical BGI is undertaken across geographies to examine historic and contemporary functions in climate resilience, in addition to modern challenges and threats. This study characterizes historical BGI and traditional ecological practices as complex interventions to support localization of the UN Sustainable Development Goals, underscoring the necessity for conservation, adaptation, and integration of traditional blue-green infrastructure practices within modern urban planning.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".