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Record W4403147596 · doi:10.1016/j.jinse.2024.100023

A novel method for rock fracturing with soundless chemical demolition agents in subzero ambient temperature

2024· article· en· W4403147596 on OpenAlexafffundabout
Elif Yapici Tanyeri, Hani S. Mitri

Bibliographic record

VenueJournal of industrial safety. · 2024
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsMcGill University
FundersNatural Resources CanadaMilli Eğitim BakanliğiNewmont Corporation
KeywordsDemolitionHydraulic fracturingEnvironmental scienceGeologyPetroleum engineeringGeotechnical engineeringMaterials scienceEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Interest in explosive-free rock fracturing has grown exponentially in the past two decades due to concerns about the environmental impacts of traditional rock fracturing methods with explosive energy. The present paper essentially presents the findings of an experimental study that aimed to develop a novel method for rock fracturing with soundless chemical degradation agents (SCDAs) at cold ambient temperatures. This is of great significance because commercially available SCDAs do not perform well, or at all, in these conditions. This study is part of a multi-phase project under the umbrella of Canada's Clean Growth Program. The newly established approach is verified through the rock fracture test results with SCDAs of two series of concrete and granite blocks exposed to cold temperatures up to −20°C. This method, the so-called high-temperature wire method (HTWM), utilizes a high-temperature spiral wire inserted into the SCDAs borehole and subjected to a DC voltage. The heat flux generated by the wire helps the SCDAs cure and expand to fracture the block. Based on the experimentally obtained results, it has been shown that the proposed new HTWM is quite promising because it enables rock fracturing with SCDAs at cold ambient temperatures reaching −20°C. It should be emphasized that such a result was previously unattainable with current industry practice.

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 score0.471
Threshold uncertainty score0.538

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)0.0000.000

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 head0.026
GPT teacher head0.279
Teacher spread0.253 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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