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Record W2377598191

A DISCUSSION ON CLASSIFICATION OF MINING-INDUCED SEISMICITY

2006· article· en· W2377598191 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

VenueChinese journal of rock mechanics and engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicGeoscience and Mining Technology
Canadian institutionsLaurentian University
Fundersnot available
KeywordsInduced seismicityRock mass classificationSeismologyTectonicsGeologyStress fieldField (mathematics)Mining engineeringGeotechnical engineeringEngineeringMathematics
DOInot available

Abstract

fetched live from OpenAlex

Currently,the different methods for classification of mining-induced seismicity have no relation with each other.In order to be convenient for prediction,prevention and control of the mining-induced seismicity,based on the latest research results on the mechanism of the disasters,the classification of mining-induced seismicity is presented in this paper,following the principles of benefiting disaster prevention and control and non-conflicting to practices adopted both at home and abroad.The concept,principle and advantages of the hiberarchy classification of the mining-induced seismicity are given.5 classes and 16 types of mining-induced seismic events are classified based on the influence of in-situ tectonic stress field,physical and mechanical properties of rocks,rock mass structures,correlation between seismicity and mining activity,source of mining-induced stress change,and the locations of seismic sources and rock mass failure.The importance of regional tectonic stress field and stress change due to mining is emphasized in the mining-induced seismicity classification,research,and control.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.287

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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