Évaluation holistique et intégrée des mesures compensatoires contre l'artificialisation des sols
Bibliographic record
Abstract
Dans la séquence Eviter-réduire-compenser, les mesures compensatoires sont les plus difficiles à appliquer. Cela s’explique par des contraintes en termes de disponibilité foncière et des connaissances limitées sur l’écosystème des sols urbains. Cet article analyse les impacts environnementaux de différentes mesures compensatoires de l’artificialisation des sols en adoptant une approche holistique et intégrée. Cette approche met en lumière les interactions complexes entre l’artificialisation et les fonctions écosystémiques des sols (stockage de carbone, maintien et production de biomasse, régulation du climat, et du cycle de l’eau). Les résultats soulignent l’importance d’une évaluation multidimensionnelle pour orienter les solutions compensatoires vers des actions durables et efficaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".