MétaCan
Menu
Back to cohort
Record W4309526579 · doi:10.1080/19236026.2022.2126924

Assessment of blast energy usage and induced rock damage in hard rock surface mines

2022· article· en· W4309526579 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

VenueCIM Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRock mass classificationExplosive materialClassification of discontinuitiesRock blastingFragmentation (computing)GeologySpecific energyMining engineeringGeotechnical engineeringPhysicsChemistry

Abstract

fetched live from OpenAlex

Explosive energy usage when fragmenting a rock mass is a complicated phenomenon. It is highly influenced by the rock mass response to higher stresses, higher loading rates, and the presence of discontinuities. An approach is presented to analyze the effects of rock mass properties on explosive energy. It is divided into steps to estimate blast energy, characterize the rock mass, assess failure mechanisms, and estimate damage zones using a combination of previously established methodologies. Through a case study in an open pit gold mine, five production shots are investigated of variable sizes with over 1,300 charged holes to analyze explosive energy–rock mass interactions. The ratio of in situ block size to the average fragmentation at variable distances from the charge is calculated to evaluate the effect of rock mass on energy distribution and fragmentation.

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 score0.203
Threshold uncertainty score0.382

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 head0.018
GPT teacher head0.236
Teacher spread0.218 · 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