MétaCan
Menu
Back to cohort
Record W2888972230 · doi:10.1016/j.dib.2018.08.151

Dataset for time-lapse ultrasonic tomography of a granite slab under uniaxial compression test

2018· article· en· W2888972230 on OpenAlexafffund
Qi Zhao, Taiming He, Johnson Ha, Kaiwen Xia, Giovanni Grasselli

Bibliographic record

VenueData in Brief · 2018
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCarbon Management CanadaEnergi Simulation
KeywordsSlabUltrasonic sensorCompression testCompression (physics)Materials scienceTest dataTomographyWaveformAcoustic emissionFinite element methodGeologyAcousticsStructural engineeringComputer scienceComposite materialEngineeringOpticsPhysicsGeophysicsTelecommunications

Abstract

fetched live from OpenAlex

This data article includes raw data for time-lapse ultrasonic tomography measurements during a uniaxial compression test. Two sets of experimental data are included: first, the ultrasonic tomography (UT) observation (i.e., waveform) data at each 20 MPa axial stress step during the uniaxial loading test; and second, the stress-strain curve of the uniaxial compression test. A numerical model based on the combined finite-discrete element method (FDEM) was used to improve the understanding of the experimental results. The model file and the simulated acoustic emission (AE) data extracted from the simulation results are also included in this article. Data sets presented in this article help to improve understanding of the progressive rock failure process at microscopic and macroscopic scales.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.822
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.026
GPT teacher head0.262
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 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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueData in BriefSame topicRock Mechanics and ModelingFrench-language works237,207