Optimisation de la granulométrie des matériaux granulaires de fondation des chaussées
Bibliographic record
Abstract
Les matériaux granulaires de fondations de chaussées jouent un rôle structural important mais sont aussi affectés par l’environnement. La granulométrie et la source de granulats vont changer la façon dont ces matériaux réagissent à ces sollicitations. Cette étude cherche donc à évaluer la performance globale des matériaux granulaires face aux contraintes mécanique et environnementale. Le module réversible, la déformation permanente, la gélivité et la conductivité hydraulique ont été mesurés pour six granulométries et trois sources. Les résultats montrent l’effet significatif de la granulométrie et de la source dans le contexte d’un fuseau granulaire et permettent de suggérer des fenêtres de performance granulométrique adaptées à des contextes de performance typiques. Ces effets de la granulométrie et de la source peuvent atteindre des facteurs 14 et 7 d’un point de sensibilité aux contraintes environnementales et des facteurs 3 et 2,5 d’un point de vue de la sensibilité aux contraintes mécaniques.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".