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Record W2065552428 · doi:10.1190/1.1845129

Ground penetrating radar (GPR) as an exploration tool in near surface pegmatite mining

2004· article· en· W2065552428 on OpenAlexaff
Jeffrey E. Patterson, Frederick A. Cook

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGround-penetrating radarPegmatiteOverburdenGeologyRadarMining engineeringBoreholeRemote sensingSeismologyMineralogyComputer scienceGeotechnical engineeringTelecommunications

Abstract

fetched live from OpenAlex

Ground penetrating radar (GPR) has proven to be a rapid and inexpensive geophysical tool for the exploration of near surface gem‐bearing pegmatites. Reasonable images and reliable interpretations have made this technique acceptable to miners for assisting in mine development. We have clearly delineated dipping and intersecting structures, crystal bearing cavities, and existing mine workings. The effects of moisture content of the scanned overburden have also been established as reducing signal transmission. Therefore, we suggest that the overburden be removed prior to scanning. Our testing has shown a rate of transmission of the GPR signal in pegmatite to be between 0.095 and 0.105 m/ns.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.032
GPT teacher head0.284
Teacher spread0.252 · 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 designObservational
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

Citations1
Published2004
Admission routes1
Has abstractyes

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