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Record W2979207349 · doi:10.1109/access.2019.2945530

Analytic Calculation of Geoelectric Fields Due to Geomagnetic Disturbances: A Test Case

2019· article· en· W2979207349 on OpenAlexafffund
D. H. Boteler, Risto Pirjola, Luis Martí

Bibliographic record

VenueIEEE Access · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsMartec (Canada)Natural Resources Canada
FundersNatural Resources Canada
KeywordsEarth's magnetic fieldGeomagnetically induced currentGeophysicsGeologyField (mathematics)Magnetic fieldGeomagnetic stormPhysicsMathematics

Abstract

fetched live from OpenAlex

Geomagnetic field variations produce geoelectric fields that can affect the operation of technological networks at the Earth's surface, including power systems, pipelines, phone cables and railway circuits. To assess the geomagnetic hazard to this technology, it is necessary to model the geomagnetically induced currents (GIC) produced in these systems during geomagnetic disturbances. This requires use of geomagnetic data with appropriate Earth conductivity models to calculate the geoelectric fields that drive GIC. To provide a way of testing geoelectric field calculation software, we provide a benchmark test case by defining a synthetic geomagnetic field variation and deriving exact analytic expressions for the Earth response based on both uniform and layered Earth conductivity models. These are then used to provide exact analytic expressions for the geoelectric fields that would be produced by the synthetic geomagnetic field variation. The synthetic geomagnetic data can be used as input to numerical geoelectric field calculation software, the output of which can be tested by comparison with the analytically-generated geoelectric fields.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
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
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.272
Teacher spread0.255 · 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 designObservational
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

Citations19
Published2019
Admission routes2
Has abstractyes

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