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Record W2000224905 · doi:10.1186/bf03353040

Static shift levelling using geomagnetic transfer functions

2014· article· en· W2000224905 on OpenAlexaff
Juanjo Ledo, Anna Gabàs, Álex Marcuello

Bibliographic record

VenueEarth Planets and Space · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsMagnetotelluricsEarth's magnetic fieldDistortion (music)LevellingFaraday cageTransfer functionGeodesyMagnetic declinationTensor (intrinsic definition)Magnetic fieldGeologyGeophysicsMathematical analysisMathematicsElectrical resistivity and conductivityPhysicsGeometryComputer scienceEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

Galvanic distortion of magnetotelluric (MT) data is a common problem in the study of the Earth’s electrical properties. These distortions are local, they affect independently each MT site, and where restricted to distortion of the electric field are manifest as vertical shifts in the apparent resistivity curves (static shift). The removal of the static shift is necessary to avoid misinterpreting MT data. We present a method that allows us to partially retrieve the regional response of the TE-mode data in a 2D case. The method determines relative changes between distortion parameters along a profile, and is based in the Faraday’s law, and uses only magnetotelluric responses: measured impedance tensor and geomagnetic transfer function (tipper). The method is valid under the assumption that the variation of horizontal magnetic field can be neglected, and a test for checking this criterion has been developed. The mathematics involved in the procedure are straightforward, and can be stated as a linear regression. We present successful applications to both synthetic and real (COPROD data) datasets.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 head0.020
GPT teacher head0.215
Teacher spread0.195 · 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 teacher head, not a consensus.

Study designOther design
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

Citations12
Published2014
Admission routes1
Has abstractyes

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