Comment Joël Noilhan a influencé la modélisation et les études en climat urbain
Bibliographic record
Abstract
Bien que ce fait soit peu connu, Joël Noilhan a commencé sa carrière non pas dans l'étude de la végétation, mais dans celle du climat urbain. Ses résultats de thèse sur les échanges radiatifs entre les différentes faces d'un bâtiment, le ciel et le sol sont encore utilisés comme hypothèses dans la plupart des modèles de canopée urbaine, comme celui développé au CNRM, Town Energy Balance (TEB). Joël a contribué dans les années 2000 à l'essor de la météorologie urbaine au CNRM. Il a notamment encouragé les collaborations internationales avec l'équipe canadienne du professeur Tim Oke, spécialiste mondial du climat urbain, et initié un volet expérimental dédié à l'urbain sur la ville de Marseille, lors de la campagne Escompte en 2001. This is not well known, but Joël Noilhan did not start his career by studying vegetation processes, but rather urban climate. His PhD results on radiative exchanges around a building still form the basis of radiative processes in most urban canopy models such as the Town Energy Balance (TEB) developed at CNRM. In the 2000s, he contributed to the urban climate studies at CNRM. He encouraged an international collaboration with the team of Tim Oke, in Canada, an international expert in urban climate. He also initiated an experimental component dedicated to urban environment over the city of Marseille, during the Escompte campaign in 2001.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".