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Record W1011636380 · doi:10.1016/j.enggeo.2015.07.020

Developing 3-D mine-scale geomechanical models in complex geological environments, as applied to the Kiirunavaara Mine

2015· article· en· W1011636380 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

VenueEngineering Geology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Geostatistics and Mapping
Canadian institutionsQueen's University
Fundersnot available
KeywordsRock mass classificationGeologyGeostatisticsMining engineeringGeomechanicsScale (ratio)Geotechnical engineeringSpatial variabilityCartographyGeographyStatistics

Abstract

fetched live from OpenAlex

An understanding of the relationship between the geological environment and rock mass behaviour induced by mining activities can lead to hazard reduction through knowledge-based design. However, characterisation of complex and heterogeneous rock masses that typify mining environments is difficult. A methodology to characterise these types of rock masses, based largely on classical statistics, geostatistics and an extension of previous quantitative structural domaining work, is presented and applied to the Kiirunavaara Mine, Sweden. In addition to a new perspective on intact rock strengths of geological units at the mine, a correlation was found between modelled volumes of clay, modelled RQD , newly identified structural domains and falls of ground. These relationships enabled development of a conceptual model of the role of geology in rock mass behaviour at the mine. The results demonstrate that the proposed methodology can be useful in characterisation of complex rock masses.

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 categoriesInsufficient payload (model declined to judge)
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 score0.355
Threshold uncertainty score1.000

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.034
GPT teacher head0.229
Teacher spread0.195 · 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