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Record W4281846508 · doi:10.1520/gtj20210038

Cemented Paste Backfill Hydraulic Conductivity Evolution from 30 Minutes to 1 Week

2022· article· en· W4281846508 on OpenAlexaff
Mohammad Shahsavari, Mohammadamin Jafari, Murray Grabinsky

Bibliographic record

VenueGeotechnical Testing Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of TorontoEnvironment and Climate Change Canada
Fundersnot available
KeywordsGeotechnical engineeringHydraulic conductivityGeologySoil waterSoil science

Abstract

fetched live from OpenAlex

Abstract The first one to four days constitutes a critical period in filling mining voids with cemented paste backfill (CPB, a mixture of mine tailings, binder, and water) and determining the saturated hydraulic conductivity (ksat) during this period is important to rational engineering design. However, most published studies started testing 24 h after specimen preparation, and those that started earlier did so by preconsolidating the specimens, which results in void ratios lower than those occurring in the field. This study uses a new test method that retains the backfill’s representative bulk properties and starts testing about one-half hour after specimen preparation. During backfilling, CPB undergoes three distinct ksat stages: a relatively constant high-value ksat stage associated with the portland cement’s (PC’s) dissolution phase; a rapidly declining ksat stage associated with PC’s acceleration phase; and then a slowly declining ksat stage associated with PC’s steady-stage and subsequent deceleration phase. The last two stages can be represented by modified Kozeny-Carman equations, in which the hydration effects are constant but different in each stage, and the ksat changes can be related to void ratio changes in each stage. In contrast, the distinct stages are not adequately represented by traditional PC “maturity” models, and, more importantly, the ksat values determined here are at least an order of magnitude higher than previously published results on similar materials.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.217
Teacher spread0.173 · 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 designSimulation or modeling
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

Citations6
Published2022
Admission routes1
Has abstractyes

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