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Record W2290926451 · doi:10.1190/tle35030270.1

Experimental aeromagnetic survey using an unmanned air system

2016· article· en· W2290926451 on OpenAlexaffabout
Alan Wood, Ian G. Cook, Buddy Doyle, Michael Cunningham, C. Samson

Bibliographic record

VenueThe Leading Edge · 2016
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsCarleton UniversityStratus Aeronautics (Canada)
Fundersnot available
KeywordsAeromagnetic surveyTakeoff and landingGeodesyGeologyAltitude (triangle)Remote sensingOffset (computer science)Aerospace engineeringMeteorologyPhysicsEngineeringComputer scienceMagnetic fieldMathematics

Abstract

fetched live from OpenAlex

Currently, Stratus Aeronautics is developing an unmanned aircraft system (UAS), called the Venturer, for aeromagnetic surveying. On 2 October 2013, in southern Alberta, Canada, an experimental survey of the UAS was conducted. Approximately 13 km of preprogrammed survey lines, along with calibration maneuvers, were flown, providing 45 minutes worth of data collection. The UAS was stable in flight and only required operator intervention for takeoff and landing. There was a + 2 m offset from the nominal altitude of 150 m, with variations of approximately ± 1.4 m. Deviations from a straight flight path were approximately ± 0.9 m along traverse lines and ± 25 m along tie-lines, the latter being affected mainly by crosswind. The noise envelope for the magnetic data acquired during the survey was approximately ± 0.05 nT, allowing a high-quality total-magnetic-intensity map to be created.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.044
GPT teacher head0.260
Teacher spread0.216 · 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 designBench or experimental
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

Citations47
Published2016
Admission routes2
Has abstractyes

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