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Record W4390344762 · doi:10.3390/min14010030

General Properties of Commingled Materials for Mix Designs

2023· article· en· W4390344762 on OpenAlexaffabout
David Barsi, G. Ward Wilson

Bibliographic record

VenueMinerals · 2023
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of Alberta
FundersFLSmidth
KeywordsTailingsEnvironmental scienceMining engineeringHydraulic conductivityPermeability (electromagnetism)WeatheringGeologySoil waterMaterials scienceSoil scienceGeochemistry

Abstract

fetched live from OpenAlex

Tailings volumes continue to collectively increase worldwide, leading to larger dams and tailings management facilities. With numerous high-profile dam failures in the past decade, the risks of these management practices are also growing. A potential shift to waste management practices at mineral mines is to commingle waste rock and dewatered tailings. This blended material should have superior physical strength properties provided by the waste rock together with improved chemical stability characteristics associated with the low permeability of the tailings. Ideally, commingled tailings and waste rock can be used to construct various mined earth landforms that are both physically and chemically stable, which will enhance operational performance and ultimately provide for the sustainable decommissioning and closure of the mining facility. To study these materials, the University of Alberta Geotechnical Centre is working with global industry partners to test commingled materials from several mine sites with varying ore and host rock types and climate regimes. The first stage of this study is described here and is focused on the optimum density, saturated hydraulic conductivity, and soil–water characteristic curves of various blend ratios, performed at laboratory scale.

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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.072
GPT teacher head0.237
Teacher spread0.165 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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