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Record W3213079134 · doi:10.1190/geo2021-0307.1

Estimating the parameters of simple models from two-component on-time airborne electromagnetic data

2021· article· en· W3213079134 on OpenAlexafffundabout
Thomas Bagley, Richard S. Smith

Bibliographic record

VenueGeophysics · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of CanadaAreva
KeywordsOverburdenResistive touchscreenSolverElectrical conductorGeologyConductivityHalf-spaceGeometryMechanicsMathematicsMaterials scienceComputer scienceGeotechnical engineeringPhysicsMathematical optimizationComposite material

Abstract

fetched live from OpenAlex

ABSTRACT The horizontal and vertical components of an on-time electromagnetic (EM) response can be used to estimate the parameters of simple models such as thin sheets, half-spaces, thin sheets over a lower half-space, and a two-layer model. The formulas used in these methods are valid in areas where the on-time response is essentially proportional to the conductivity or conductance, the so-called “resistive limit.” The half-space and thin sheet over lower half-space models can be combined to give an estimate of the conductivity for a lower half-space below a thick sheet that might be reasonable for the entire survey area. With this estimation, an equation solver can be used to estimate the thickness and conductivity of the overlying thick sheet over the whole survey area. This latter approach seemed most appropriate for the Russell South area in the Athabasca Basin, Canada, where GEOTEM data have been collected. The output of the algorithm was generally stable. Although it did not always reliably reproduce the overburden thicknesses as measured in a set of reference drillholes, it did give an estimate that was reasonable in the relatively conductive areas.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.550

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.254
Teacher spread0.218 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2021
Admission routes3
Has abstractyes

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