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Record W3043632161 · doi:10.3808/jeil.202000030

Wormhole Network Imaging in Post-Chops Process Using Ground-Penetrating Radar

2020· article· en· W3043632161 on OpenAlexafffund
Xiaocheng Zhou, Guohe Huang, Chunjiang An, Chaofeng Lü, Yuan Yao, P. Zhang, X. J. Chen, Yanqi Wu, Jian Shen

Bibliographic record

VenueJournal of Environmental Informatics Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsConcordia UniversityUniversity of Regina
FundersPetroleum Technology Research CentreNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsGround-penetrating radarWormholeRadarDimension (graph theory)Antenna (radio)Oil fieldProcess (computing)Computer scienceGeologyEnvironmental scienceRemote sensingPetroleum engineeringTelecommunicationsPhysicsMathematics

Abstract

fetched live from OpenAlex

In this study, an integrated near-field GPR system (INFGPRs) with a frequency tunable patch antenna was developed to investigate wormholes in oil sand that emerge during CHOPS. 3-dimension imaging of wormholes was then obtained based on INFGPRs through step-scanning against the wall of inspection wells. The feasibility and availability of the developed GPR system were identified through bench-scale simulation in the lab. In addition, a factorial factor experiment was designed in order to reveal the interaction effects of impact factors (depth, size, and content of wormhole) on the performance of the developed GPR system.

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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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