Mapping the city's trajectories to cool the city and better resist heat waves
Bibliographic record
Abstract
Abstract. During the months of July and August 2021 and 2022, numerous climatic disturbances such as heat domes in Canada, floods and landslides in Belgium, Germany, Turkey, China as well as giant fires in Russia and Greece have marked the news and people’s minds. Climate inertia and the complexity of changing the world’s energy model make it necessary to adapt territories to limit the impacts of the changes that are underway. However, if the speeches on the urgency to act to cool the cities have become omnipresent, the implementations of solutions seem limited, and some territories even seem to be going in the opposite direction by massively artificializing the edges of urban areas. The objective of the FreshWay research project is to identify and analyse planning and implementation on the ground to combat summer heat waves and to represent the adaptation trajectories of cities. The first information that is questioned is the evolution of urban vegetation insofar as plants provide shade and allow cooling through the process of evapotranspiration. The paper presents the required information and the data model, cases study, the process to integrate data, the choice of indicators and the construction of trajectories from different perspectives for the municipality of Castelnau-le-lez, Sarcelles and Pontault-Combault, and at different level of details.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".