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Record W2318834126 · doi:10.4133/sageep.28-005

Airborne Geophysics, Remote Sensing, UAV (Drone)-based Surveys and Mining Geophysics

2015· article· en· W2318834126 on OpenAlexaffabout
Andi Pfaffhuber, Helgard Anschuetz, Hamed Rafezi, Alexandre Novo, Ferri Hassani, Kurt Sørensen, Weiqiang Liu, Rujun Chen, Hong Wu, Jieting Qiu, Hongchun Yao, Ruijie Shen, Qiang Ren, Fuguo Chang, Pei Zeng, Luo Weibin, Greg Hodges, Douglas Garrie, Craig W. Christensen, Jean M. Legault, David Toop, Greg A. Oldenborger, Geoffrey Plastow, Nasreddine Bournas, Zihao Han, Marta María González Orta, Isaac Fage, Tianyou Chen, Leif H. Cox, Masashi Endo, Michael S. Zhdanov, Jeffrey G. Paine, Edward W. Collins, Lucie Costard, Vikas Chand Baranwal, Jan Steiner Rønning, Inger‐Lise Solberg, Einar Dalsegg, Jan Fredrik Tønnesen, Shakeel Ahmed

Bibliographic record

VenueSymposium on the Application of Geophysics to Engineering and Environmental Problems 2015 · 2015
Typearticle
Languageen
FieldEngineering
TopicAutomated Road and Building Extraction
Canadian institutionsGeological Survey of CanadaUniversity of CalgaryQueen's UniversityMcGill University
Fundersnot available
KeywordsDroneRemote sensingGeologyComputer scienceGeophysics

Abstract

fetched live from OpenAlex

The individual abstracts for this session are available to read in the PDF.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.127
Threshold uncertainty score0.426

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1270.048

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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes2
Has abstractyes

Explore more

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