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Record W2523135885 · doi:10.1190/geo2016-0119.1

Fitting superparamagnetic and distributed Cole-Cole parameters to airborne electromagnetic data: A case history from Quebec

2016· article· en· W2523135885 on OpenAlexaboutno aff
James Macnae

Bibliographic record

VenueGeophysics · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsInduced polarizationSuperparamagnetismPolarizabilityPermeability (electromagnetism)ConductivityPolarization (electrochemistry)Electrical conductorIntrusionGeologyMaterials scienceComputational physicsCondensed matter physicsPhysicsMagnetizationElectrical resistivity and conductivityChemistryMagnetic fieldQuantum mechanicsGeochemistry

Abstract

fetched live from OpenAlex

ABSTRACT Our aim was to confirm the ability of polarizable and superparamagnetic (SPM) thin sheets in the near surface to improve the model fit of airborne electromagnetic data. Our method was to fit induced polarization (IP) effects with Cole-Cole complex conductivity and fit SPM effects with Chikazumi complex permeability. Surficial conductors were assumed to be the source of the conductivity and IP effects. In this case history from Lac Brûlé, Quebec over an anorthosite intrusion, small to large IP effects were found to be essential to fit most of the observed data. In some areas, it was also possible to separate SPM effects from IP effects in the data. Most IP effects in this unusually polarizable area were adequately fit with a distributed decay characterized with a frequency dependence of c=0.3, but some required a sharper response characterized by c=0.8. In general, fitted IP time constants were anticorrelated with fitted frequency dependence, with short time constants fitted to the larger c values and vice versa. SPM effects were detected in a small but significant fraction of the data, and appear to be spatially related to static magnetic anomalies. The SPM in this case is presumably related to fine-grained rock magnetism, rather than the more common case of weathering products.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.920

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 head0.025
GPT teacher head0.220
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations20
Published2016
Admission routes1
Has abstractyes

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