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Record W3095880688 · doi:10.1017/exp.2020.32.pr2

Review 2: Effect of rapid cooling on rock strength reduction after microwave irradiation

2020· peer-review· en· W3095880688 on OpenAlexfundaboutno aff
E. Jerby

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUltimate tensile strengthExcavationMicrowaveMicrowave irradiationStrength reductionMining engineeringMaterials scienceEnvironmental scienceGeotechnical engineeringEngineeringForensic engineeringComposite materialStructural engineeringTelecommunications

Abstract

fetched live from OpenAlex

A variety of machines are currently being used for mechanical excavation in mining and civil industries. A series of research works have been conducted at McGill University in the past decade to study the effects of microwave (MW) irradiation on rock mechanical properties. The idea is to enhance the excavation performance by improving the rate of penetration and decreasing the wear rate on the cutting tools. These two effects would eventually translate into economic benefits for mine operators. The effectiveness of MW on weakening rocks is proven, however the most efficient method to employ MW in mines is still under investigation. This article presents some experimental results on the effects of cooling- rate on rock strength. Brazilian Tensile Strength (BTS) of microwave treated samples were compared in natural air-cooled and water rapid-cooled conditions.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.004

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 head0.009
GPT teacher head0.245
Teacher spread0.236 · 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.

Study designNot applicable
DomainEvaluation
GenreOther

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
Published2020
Admission routes2
Has abstractyes

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