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Shear rupture – two case studies from a deep mine

2014· article· en· W2623046060 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

VenueDeep mining · 2014
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsLaurentian UniversityWomen's and Gender Studies et Recherches FéministesGolder Associates (Canada)
Fundersnot available
KeywordsGeologyMicroseismShear (geology)BrittlenessSlip (aerodynamics)Fault (geology)SeismologyRock mass classificationShear stressStress fieldGeotechnical engineeringMechanicsStructural engineeringPetrologyEngineeringMaterials scienceFinite element methodComposite material

Abstract

fetched live from OpenAlex

When a fault-slip event mechanism is determined, it is easy to assume that there must be a fault present to generate the event. A pre-existing fault is not needed to create a fault-slip event. Fault rupture (the failure process in brittle rocks which occurs under confined conditions) also generates a fault-slip focal mechanism. Fault ruptures will commonly occur far from mining, in abutments, wide pillars, and sills. First the rupture process is overviewed. Then two mining fault rupture case examples are summarised to show the fault rupture process, final rupture geometry, and energy release potential observed in the field conform to theoretical understanding. In this article it is shown that the fault rupture process and energy release depends on the boundary condition (system stiffness) surrounding the failure process. While seemingly theoretical, the findings have practical significance with respect to rock mass characterisation, microseismic monitoring, rock mass behaviour back analysis, and mining strategy.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.628

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 head0.020
GPT teacher head0.251
Teacher spread0.231 · 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