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Record W2967632021 · doi:10.3997/2214-4609.201901667

The Value of Seismics in Mineral Exploration and Mine Safety

2019· article· en· W2967632021 on OpenAlexaboutno aff
M. Manzi, Alireza Malehmir, Raymond Durrheim

Bibliographic record

Venue81st EAGE Conference and Exhibition 2019 · 2019
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyMining engineeringMineral explorationMineral resource classificationDrillingReflection (computer programming)Earth scienceGeochemistryEngineeringComputer science

Abstract

fetched live from OpenAlex

Summary The word “seismics” in the geoscience community is often used synonymously with “oil and gas”, despite its successes in other applications, for example, in mineral exploration, engineering application, mine planning and safety.Over the past few decades, the method has been developed and successfully used for mineral exploration, mine planning, and safety in “hard rock” metallogenic provinces worldwide (e.g., Australia, Europe, Canada, and South Africa), leading to the discovery of giant minerals and metal deposits.However, despite these successes, the method's capabilities in mining still remains less-known to many geoscientists and some mining companies are still reluctant to use it for “hard rock” exploration and mining.The purpose of this paper is to demonstrate how the reflection seismic method has been successfully used to explore and discover some of the world's largest mineral and metal deposits that are located deep underground - where exploration drilling is more costly and risky. A wide range of case studies from hard rock environments are covered, for example, from South Africa and Canada.

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 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 score0.137
Threshold uncertainty score0.267

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 head0.009
GPT teacher head0.190
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 teacher head, not a consensus.

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

Citations4
Published2019
Admission routes1
Has abstractyes

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