Bibliographic record
Abstract
La caractérisation granulaire de dépôts hétérométriques organisés en paquets glissés implique une démarche systématique afin d'optimiser l'extraction de données et de les rendre comparables. La quantification des rapports structures-fractions granulaires a permis de mettre en évidence des cinématiques localisées, inscrites dans des cinématiques d'ordre I. Les rides de Tutupaca, le lobe distal du Pichu-Pichu, l'effondrement du Misti au Pérou; la levée latérale du Cheix et le charriage distal de Perrier aux Mt Dore ; les transformations de Meager au Canada, de la Qda San Lazaro au Pérou, de Pardines en France ont été quantifiés. Héritage volcanique, ségrégations granulaires, cataclases syn-tectoniques sont des précurseurs aux modifications matricielles, à l'origine des transformations distales en lahars. Les données ainsi comparées permettent de proposer des classifications sédimentologiques distinguant les effondrements proximaux des transformations par les fluides ( hydrothermaux, eau). Seules quelques équations regroupent l'ensemble de ces dépôts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".