Revue de divers aspects liés à la stabilité géotechnique des ouvrages de retenue de résidus miniers: Partie II – Analyse et conception
Bibliographic record
Abstract
Tailings dikes are still prone to relatively frequent failures due to geotechnical instability. The general problem related to stability of such retaining works has been presented in part I. In this part II, the main factors that affect the stability of dikes are reviewed. Typical tools used to analyse the behavior of these engineering works are described, with an emphasis on the effects of critical events such as large precipitations and earthquakes. The article also discusses new avenues that help control the problems, including the use of waste rock inclusions in the tailings impoundment. Les digues des parcs à résidus miniers sont, encore aujourd’hui, sujettes à des défaillances relativement fréquentes suite à des instabilités géotechniques. La problématique générale liée à la stabilité de ces ouvrages de retenue a été présentée dans la partie I. Dans cette partie II, on revoit les principaux facteurs qui affectent la stabilité des digues. Les outils typiquement utilisés pour analyser le comportement des ouvrages sont décrits, en mettant l’emphase sur l’effet des événements critiques comme les pluies abondantes et les séismes. L’article discute aussi de nouvelles avenues pour aider à contrôler certains problèmes, incluant l’utilisation d’inclusions de roches stériles dans les parcs à résidus miniers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".