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Record W4200094649 · doi:10.18280/acsm.450505

Acoustic Emission Features of Anthracite under the Influence of Loading Rate

2021· article· en· W4200094649 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2021
Typearticle
Languageen
FieldEngineering
TopicGeoscience and Mining Technology
Canadian institutionsnot available
Fundersnot available
KeywordsAcoustic emissionAnthraciteBrittlenessMaterials scienceGeotechnical engineeringStrain rateCoalComposite materialGeologyEngineering

Abstract

fetched live from OpenAlex

Loading rate is an important impactor of the mechanical properties, as well as the deformation and failure mode of coal and rock. Using an RMT-301 rock mechanics tester and a Soft Island acoustic emitter, uniaxial compression and acoustic emission (AE) tests were carried out on coal samples under different loading rates. The results show that uniaxial compressive stress-strain curves of the rock samples each consist of four segments: compaction, elasticity, yield, and failure. As the loading rate increased from 0.01mm/s to 0.02mm/s, the peak strength rose, the post-peak deformability dropped, the brittle failure features of anthracite became more obvious, more AE events took place, and AE frequency increased. Energy analysis shows that, the faster the loading rate, the larger the AE count, the faster the energy accumulation, but the fewer the total energy accumulation.

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)0.000
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 score0.138
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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