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Record W4321788485 · doi:10.1520/gtj20220124

Integrated Interpretation of Electrical Conductivity Changes, Heat Generation, and Strength Development in the First Week in Cemented Paste Backfill

2023· article· en· W4321788485 on OpenAlexaff
Mohammadamin Jafari, Murray Grabinsky, Wendal Yue

Bibliographic record

VenueGeotechnical Testing Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVicat softening pointGeotechnical engineeringCompressive strengthWater contentMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

ABSTRACT Underground mining operations need nondestructive test methods to assess placed backfill strength development at early age (especially up to 1 week of curing time) so that they can continue proximate mining quickly. Recent developments in electrical conductivity (EC) transducers for field applications offer this possibility, but the EC measurements must be correlated to backfill strength. To determine the feasibility of this approach, a laboratory test program used a mine’s backfill materials mixed with varying water and binder contents and tested these over a 7-day period. Strength was characterized using unconfined compressive strength (UCS) tests and correlated to EC. Comparisons were also made to complementary test results using Vicat and heat generation measurement techniques. Strong and consistent correlations were determined between EC and UCS for a given binder content and water content. But moreover, a lower-bound correlation was determined and could also be used in the field if as-placed properties vary to an unknown extent within the limits of parameters considered in the study. This provides the basis to use EC measurements in the field and confidently assess the backfill’s minimum attained strength in real time.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.055
GPT teacher head0.232
Teacher spread0.177 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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