Étude de la compaction des milieux granulaires : de l'échelle locale à l'échelle globale
Bibliographic record
Abstract
La densification lente d'un empilement granulaire sous sollicitations presente un double interet. D'une part, la compaction douce est un processus industriel courant. D'autre part la compaction d'un empilement s'avere tres analogue a l'evolution d'un systeme vitreux. La densification a ete etudiee experimentalement par gamma-densimetrie. Conjointement, la visualisation de la position des grains au coeur de l'empilement est possible grâce a une technique d'imagerie en fluide iso indice. Ces etudes experimentales furent completees par diverses simulations numeriques de la compaction. L'evolution de la compacite est bien decrite par une loi en exponentielle etendue, issue de la dynamique des verres. Le temps caracteristique de la compaction suit une loi d'Arrhenius ce qui caracterise le comportement des verres forts. Outre la compaction, de la convection est aussi observee dans le milieu et son influence sur la compacite a ete etudiee. L'etude menee a l'echelle du grain a permis de montrer que les deplacements des grains presentent des caracteristiques communes a tous les systemes hors equilibre : distributions des mouvements non gaussiennes, intermittence. . . Elle a etabli que certains evenements rares, de grandes amplitudes, influent profondement sur la dynamique du systeme. L'etude de la structuration de l'empilement a l'echelle locale a aussi fourni une explication aux effets memoire observes dans les milieux granulaires.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".