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Revue de divers aspects liés à la stabilité géotechnique des ouvrages de retenue de résidus miniers: Partie II – Analyse et conception

2013· article· fr· W3174499165 on OpenAlexaff
Michel Aubertin, Montoya Lerma James, M. Mbonimpa, Bruno Bussière, R.P. Chapuis

Bibliographic record

VenueEnvironnement Ingénierie & Développement · 2013
Typearticle
Languagefr
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversité du Québec en Abitibi-TémiscaminguePolytechnique Montréal
Fundersnot available
KeywordsHumanitiesDikeGeographyGeologyPhilosophyGeochemistry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.019
GPT teacher head0.248
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

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