Nantes - how can the city be built for the climate? in: Cities and climate change : urban heat islands : Barcelona, Lyon, Marseille, Montréal, Nantes, Rennes, Roma, Stuttgart, Toulouse, Wien
Bibliographic record
Abstract
How can the city be built for the climate? For Nantes Métropole, the public body responsible for inter-municipal cooperation and urban and climate policy, this question is still highly relevant. Despite being a pioneer in France in the fight against climate change, Nantes' climatic, geographic and urban situation have led urban and climate issues separated, albeit by a fine line. The prospect of climate change requires a reconsideration of how the city is built. This must be based, in particular, on two inter-related questions: Do we know how to build the city for the current climate? Followed by, will we know to build the city for the climate of tomorrow?The approach of the climate Plan (Plan climat) being implemented in Nantes and the partnerships formed with local research institutions provide us with some of the initial answers to these questions. With regard to the current climate, research projects on bioclimatic design have been carried out at district level. For the climate of tomorrow, research on vegetation and greening strategies will help us to address the urban heat island issue more successfully.However Nantes' experiments have revealed certain limitations concerning, for example, whether the approach could be applied elsewhere, and how research expertise can be transferred to the practical field of urban engineering. These new problems highlight a need to bridge the gap between researchers and planning professionals to ensure that urban heat islands are dealt with in a concrete and pragmatic manner at local level.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".