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Record W2897640483 · doi:10.25103/jestr.083.28

Experimental I nvestigations into the Effects of Lithology on Acoustic Emission

2015· article· en· W2897640483 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.

Bibliographic record

VenueJournal of Engineering Science and Technology Review · 2015
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Regina
FundersNational Natural Science Foundation of China
KeywordsAcoustic emissionLithologyGeologyBasaltRock burstMineralogyGeochemistryMaterials scienceComposite materialChemistry

Abstract

fetched live from OpenAlex

In order to study how lithology affects acoustic emissions (AE), a series of tunnel rock burst simulation experiments, monitored by acoustic emission instruments, were conducted on granite, marble and basalt. By analyzing the characteristic parameters, this study found that AE events occur more frequently during the rock burst process on granite and basalt. Marble remains dormant until 75% of the loading time before the peak, at which point, cracks develop rapidly and AE events dramatically increase. During the rock burst process, the AE energy release demonstrates that low energy is released in the incubation phase and robust energy is released during the later phase. Before the rock burst occurs, increased in the heterogeneity index C v values of the AE event are subject to lithology. The C v values of granite and basalt have an increase of about 0.2-0.4, while marble shows an increase of 1.0-1.2. The heterogeneity index C v value of an AE event is in line with the rock burst process.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.164
Threshold uncertainty score0.176

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.012
GPT teacher head0.259
Teacher spread0.247 · 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