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Record W1995455859 · doi:10.1139/t09-126

Mechanical response of highly gap-graded mixtures of waste rock and tailings. Part I: Monotonic shear response

2010· article· en· W1995455859 on OpenAlexafffundvenue
Ali Khalili, Dharma Wijewickreme, G. Ward Wilson

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTailingsGeotechnical engineeringConsolidation (business)GeologyRock mass classificationShear (geology)CompactionMining engineeringMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

The idea of mixing mine tailings and waste rock to form “paste rock” prior to disposal is now receiving significant attention from the point of view of sustainable mine waste management practices. This approach has been viewed as a favourable alternative to traditional methods of mine waste disposal because paste rock has the potential to overcome deficiencies (e.g., acid rock drainage and liquefaction-induced failures) associated with traditional methods. To advance the current limited knowledge, a laboratory research program was undertaken to study the mechanical response of paste rock. Testing was undertaken on paste rock specimens prepared so that the tailings would “just fill” the void spaces within the waste rock particle skeleton. The findings suggest that the material is unlikely to experience flow deformation under monotonic shear loading conditions, at least up to the tested initial effective confining stress conditions of 400 kPa. The monotonic shear response of paste rock was found to be similar to that of rock-only material; this finding is in agreement with previous observations from one-dimensional consolidation testing where it had been shown that the rock particle skeleton would carry almost 90% of the externally applied stress on a given paste rock mass.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations29
Published2010
Admission routes3
Has abstractyes

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