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Record W2058604350 · doi:10.1520/gtj100054

Shear Testing of Soft Rock Masses

2006· article· en· W2058604350 on OpenAlexaff
Jerry Szymakowski

Bibliographic record

VenueGeotechnical Testing Journal · 2006
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsRock mass classificationDirect shear testGeologyShearing (physics)Geotechnical engineeringShear (geology)SiltstoneJoint (building)PetrologyEngineeringStructural engineeringStructural basin

Abstract

fetched live from OpenAlex

Abstract Previous studies aimed at improving our understanding of rock mass behavior have incorporated laboratory techniques, case studies, or numerical methods. This study extends the laboratory based data by examining the behavior of relatively large scale jointed, soft rock mass samples in direct shear. The rock mass samples tested in this study were fabricated by cutting joint sets into a soft, synthetic siltstone block. Therefore, the development of new procedures and equipment for fabricating and shear testing the rock mass samples was required. The visual and measured data recovered from this testing was used to identify and model the prepeak behavior and failure mechanisms occurring within the samples. The observed prepeak behavior of the rock masses was found to comprise either sliding along one of the joint sets or rotation of a portion of the rock mass defined by the jointing pattern. All samples ultimately failed by shearing through intact rock coincident with the shear plane defined by the testing apparatus.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.030
GPT teacher head0.229
Teacher spread0.199 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations6
Published2006
Admission routes1
Has abstractyes

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