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Use of unmanned aerial vehicles to supplement conventional investigation methods for underground open void stability and mitigation

2017· article· en· W2769632744 on OpenAlexafffund
Ryan Preston, Justin Roy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsGolder Associates (Canada)
FundersIndigenous and Northern Affairs Canada
KeywordsBoreholeExcavationMining engineeringLidarVoid (composites)Environmental scienceScope (computer science)GeologyEngineeringRemote sensingComputer scienceGeotechnical engineering

Abstract

fetched live from OpenAlex

As part of the scope to investigate the stability of and remediation options for a large underground open void (stope) complex at the Giant Mine Remediation Project (an inactive underground gold mine in the Northwest Territories), Golder’s project team built and flew a custom unmanned aerial vehicle (UAV) in order to inspect inaccessible areas of the stope and connected development workings. The UAV surveys supplemented traditional investigation techniques including borehole-based camera surveys and LIDAR scanning (cavity monitoring system, CMS) and was successful in filling information and data gaps while reducing overall investigation costs. 3D geometry models built from UAV flight video were found to accurately reproduce the shape of the excavation when compared to incomplete CMS data collected from boreholes that intersected the void.

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)0.000
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.188
GPT teacher head0.370
Teacher spread0.182 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

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