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Record W6931614471 · doi:10.5683/sp/4rfhbj

Analysis of Strainbursts in the Sudbury Region and Numerical Modelling of Destress Blasting

2017· dataset· en· W6931614471 on OpenAlexaffabout

Bibliographic record

VenueQSpace (Queen's University Library) · 2017
Typedataset
Languageen
FieldEngineering
TopicPolymer-Based Agricultural Enhancements
Canadian institutionsQueen's University
Fundersnot available
KeywordsBoreholeReplicateRock blastingInstrumentation (computer programming)Underground mining (soft rock)Numerical modelsCode (set theory)Interpretation (philosophy)

Abstract

fetched live from OpenAlex

The occurrences of strainbursting in underground mining have long presented a risk to workers underground. The investigation of these events occurred in the Sudbury basin between 2013 and 2015, yielding results in the nature of observations, numerical models, and a database of events. Included in the data are videos of borehole observations, instrumentation data, code to replicate the numerical models used in the analysis of destress blasting, and a database containing the author's interpretation of many strainburst event reports. All of the data contained was used in the author's Master's of Applied Science thesis, "The Analysis of Strainbursts in the Sudbury Region And Numerical of Blasting". The findings from this research and associated data set contained herein, will add to the continuous process of improving the understand and prevention of strainbursts in mines.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.883
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.007

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 head0.014
GPT teacher head0.194
Teacher spread0.180 · 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 designSimulation or modeling
Domainnot available
GenreDataset

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
Published2017
Admission routes2
Has abstractyes

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