Flux and turbulence measurements at a densely built‐up site in Marseille: Heat, mass (water and carbon dioxide), and momentum
Bibliographic record
Abstract
Eddy covariance (EC) observations above the densely built‐up center of Marseille during the Expérience sur site pour contraindre les modèles de pollution atmosphérique et de transport d'émissions (ESCOMPTE) summertime measurement campaign extend current understanding of surface atmosphere exchanges in cities. The instrument array presented opportunities to address issues of the representativeness of local‐scale fluxes in urban settings. Separate EC systems operated at two levels, and a telescoping tower allowed the pair to be exposed at two different sets of heights. The flux and turbulence observations taken at the four heights, stratified by wind conditions (mistral wind and sea breeze), are used to address the partitioning of the surface energy balance in an area with large roughness elements. The turbulent sensible heat flux dominates in the daytime, although the storage heat flux is a significant term that peaks before solar noon. The turbulent latent heat flux is small but not negligible. Carbon dioxide fluxes show that this central city district is almost always a source, but the vegetation reduces the magnitude of the fluxes in the afternoon. The atmosphere in such a heavily developed area is rarely stable. The turbulence characteristics support the empirical functions proposed by M. Roth.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".