Urban Participation + Research + Regulation Method (PRRM) to Broadly Implement Green Urban Infrastructure Solutions
Bibliographic record
Abstract
the 2030 Agenda shows the path to achieve the sustainable development goals. in addition, the international paris Agreement, the ipcc reports on climate change and the recent cop26 in glasgow urge the international community to decarbonize their economies and move towards carbon neutral countries by 2050.As urban designers, willing to meet these international commitments through our profession, green urban infrastructure solutions (gUis) evidence cost-efficient policy tools to respond to climate change.this paper includes the implementation of gUis in two pilot projects in the Basque country. in addition, the environmental benefits derived from such green intervention are analyzed, in terms of climate change adaptation, including the amelioration of stormwater runoff, reduction of urban hot spots and improvement of urban air quality.the paper also highlights the barriers and difficulties encountered when implementing these gUis into practice.this includes the skepticism about innovative urban solutions and the lack of experience in gUis.therefore, the paper proposes an urban participation, research and regulation method in order to overcome current barriers and enhance a broad implementation of gUis to comply with international commitments.
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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 | 0.085 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".