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Record W4386926075 · doi:10.1080/19236026.2023.2239106

Development of a novel cartridge for expansive cement application to hard rock breakage

2023· article· en· W4386926075 on OpenAlexaff
Tuo Chen, Yizhuo Li, Kelly-Meriam Habib, Hani S. Mitri

Bibliographic record

VenueCIM Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsMcGill University
Fundersnot available
KeywordsCartridgeGeologyMaterials scienceGeotechnical engineeringMetallurgy

Abstract

fetched live from OpenAlex

Expansive cement (EC) is generally a slurry that it is poured into vertical holes for surface rock breakage applications. This paper describes the development of a novel cartridge for extending EC applications from gravity-filled vertical holes to horizontal, uptilted, and wet boreholes. Four cartridge prototypes were made from low-cost and readily available plastics using three-dimensional printers. The performance of each cartridge was investigated in unconfined rock slab tests. The polylactic acid (PLA) cartridge was found to be superior to the thermoplastic polyurethane, polyethylene terephthalate glycol, and acrylonitrile butadiene styrene cartridges. Through partial heat containment, the PLA cartridge accelerated the EC hydraulic reaction and shortened the onset of rock destruction by 30% relative to vertical, gravity-filled EC. Finally, rock breakage with EC was demonstrated in an underground mine using PLA cartridges. This novel type of cartridge could not only suit various applications beyond the scope of the current EC surface applications but also significantly improve the rock fracturing efficiency of EC.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.255
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2023
Admission routes1
Has abstractyes

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