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Record W2331635076 · doi:10.1190/1.3513664

Electromagnetic scattering by thin resistive bodies

2010· article· en· W2331635076 on OpenAlexafffund
Andrei Swidinsky, Nigel Edwards

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsResistive touchscreenScatteringMaterials scienceOpticsPhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Controlled‐source electromagnetic prospecting systems which produce vertical current flow in the earth are sensitive to horizontal, electrically resistive structures such as hydrocarbon deposits and fresh water lenses. The electromagnetic response of such three‐dimensional targets can sometimes be approximated by the fields produced by an arrangement of thin resistive sheets buried in a conductive host medium. We have previously shown, through simplified two‐dimensional modeling, that the resistive sheet problem is more subtle than the conductive case commonly used in mineral exploration. Not only does the resistive sheet require the continuity of the normal current density as opposed to the continuity of the tangential electric field, all interaction terms within the coupling matrix must also be calculated in full rather than approximated by point sources representing each element. The purpose of this paper is to extend these results to the more realistic and practical situation of a three‐dimensional resistive sheet buried in a multilayered earth. As an example of the use of the algorithm, the marine controlled‐source electromagnetic response of a simple anticlinal hydrocarbon reservoir underlying near surface resistors such as shallow gas or gas‐hydrate deposits is modeled. Results show that such near surface anomalies, even those with low deflection numbers, significantly affect the response of the deeper target, and must be included in modeling and interpretation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.217

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)0.0000.000

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 head0.005
GPT teacher head0.223
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 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

Citations0
Published2010
Admission routes2
Has abstractyes

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