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Record W2182917839 · doi:10.14288/1.0107735

The drained stacking of granular tailings : a disposal method for a low degree of saturation of the tailings mass

2011· article· en· W2182917839 on OpenAlexaff
Joaquim Pimenta de Ávila

Bibliographic record

VenueOpen Collections · 2011
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTailingsSiltSaturation (graph theory)Tailings damGeotechnical engineeringGeologyDegree of saturationEnvironmental scienceMining engineeringSoil scienceSoil waterMetallurgyMaterials science

Abstract

fetched live from OpenAlex

The method of tailings disposal, has a strong influence on the characteristics of the tailings mass within the reservoir and on the behaviour of the tailings dam. The presence, and the amount of the water in the voids of the tailings, is an important factor that governs several aspects of the performance and the safety of tailings dams. A tailings mass with a low degree of saturation presents a lower risk of liquefaction of the tailings mass and achieves higher densities, in response to the loads applied by the reservoir filling. This paper describes a method of disposal of tailings composed of silt and a fine sand fraction, with provisions to drain the water from the voids of the tailings. The starter dam is a construction controlled pervious dam with a drainage system provided within the tailings deposition area, in order that the tailings mass achieves a low degree of saturation. Two examples are presented of tailings disposal systems using this method, with stack structures achieving up to 195.0 meters height. [All papers were considered for technical and language appropriateness by the organizing committee.]

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.869
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.242
Teacher spread0.210 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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