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Record W3115415018 · doi:10.1155/2020/6666548

Analytical Assessment of Internal Stress in Cemented Paste Backfill

2020· article· en· W3115415018 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsLakehead University
FundersEducation Department of Shaanxi ProvinceNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of Shaanxi ProvinceChina Postdoctoral Science Foundation
KeywordsMaterials scienceStress (linguistics)Internal stressSensitivity (control systems)Stress fieldGeotechnical engineeringComposite materialStructural engineeringFinite element methodGeologyEngineering

Abstract

fetched live from OpenAlex

To analytically describe the internal stress in a fill mass made of granular man‐made material (cemented paste backfill, CPB), a new 3D effective stress model is developed. The developed model integrates Bishop effective stress principle, water retention relationship, and arching effect. All model parameters are determined from measurable experimental data. The uncertainties of the model parameters are examined by sensitivity analysis. A series of model application is conducted to investigate the effects of field conditions on the internal stress in CPB. The obtained results show that the proposed model is able to capture the influence of operation time, stope geometry, and rock/CPB interface properties on the effective stress in CPB. Hence, the developed model can be used as a useful tool for the optimal design of CPB structure.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.325

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 communication0.0000.001
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 head0.011
GPT teacher head0.245
Teacher spread0.235 · 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