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Record W4256709545 · doi:10.5194/gi-2015-45

Magnetic Airborne Survey – Geophysical Flight

2016· preprint· en· W4256709545 on OpenAlexaff
Erick camara, S Guimaraes

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicGeography and Environmental Studies
Canadian institutionsBruce Power (Canada)
Fundersnot available
KeywordsGeophysicsContext (archaeology)MagnetometerFluxgate compassMagnetic surveyRemote sensingGeologyMeteorologyGeographyPhysicsMagnetic fieldArchaeologyMagnetic anomaly

Abstract

fetched live from OpenAlex

Abstract. Geophysics is a Geoscience that involves the study of the Earth via physical measurements. In this context, there are many types of physical measurements that can be studied. Airborne geophysics involved one of these types of measurements. It uses airborne data to characterize larger areas with mineral exploration potential. Measurements are typically taken at a preliminary point of the exploration process, after the soil of the area has been classified. The first geophysical method to utilize airborne research was the magnetic method. Discovered by Faraday, Sect. XIX, the method was initially used by the USSR (current day Russia) in 1936 (Hood, 1969) and better adapted by America in 1940 (Hood, 1969). Both countries had a vested military interest in the technology, particularly for submarine applications. After some adaptations, another early flight was made in the US in 1944 using the Beech Staggerwing NC18575 (Morrison, 2004). The first geophysical airborne survey in Brazil occurred 60 years ago (1953) in the city of Sao Joao Del Rey, Minas Gerais (Hildebrand, 2004). It was conducted by the Prospec Company, which later became Geomag. The survey utilized both magnetic and radiometric methods. The fixed wing aircraft used in the survey was the PBY-5 (Catalina). It was equipped with a Fluxgate magnetometer, which measured the total magnetic field, in the tail of the aircraft (Hildebrand, 2004). The system was totally analogic and constructed using electromechanical units and an infinite series of valves. All the data processing was done manually because, at that time, analogic data was recorded, tabulated, corrected, interpolated and plotted on a cartographic base. The data were then presented in the form of a profile overlay on contour maps. All tracing was also manually completed.

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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0450.029

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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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