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Record W4407829762 · doi:10.1109/tpwrd.2025.3544488

The Influence of the Geoelectric Coast Effect on Geomagnetically Induced Currents

2025· article· en· W4407829762 on OpenAlexaff
Darcy Cordell, Martyn Unsworth

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeomagnetically induced currentGeophysicsGeologyCurrent (fluid)Environmental scienceEarth's magnetic fieldPhysicsGeomagnetic stormOceanographyMagnetic field

Abstract

fetched live from OpenAlex

Geomagnetically induced currents (GICs) in power networks can damage transformers, cause voltage instability and lead to power outages. GICs are driven by an induced voltage in transmission lines due to the induced surface geoelectric field component parallel to the line. It is well-known that an electrically conductive ocean can increase the geoelectric field magnitude on the landward side of the coast. However, limited work has been done to elucidate how the adjacent ocean impacts network GICs. We model GICs using a well-known network model situated adjacent to an ocean. Contrary to the notion that GIC risk is higher in coastal areas, we show that the ocean can cause a decrease in the maximum possible GIC in coastal power networks relative to calculated GICs which exclude coast effects, while increases in GIC due to the ocean can be relatively modest. This is because the geoelectric field only increases in the component perpendicular to the coast but decreases parallel to the coast. Thus, transmission lines parallel to coastlines experience a net decrease in induced voltage along their entire length, while transmission lines perpendicular to coastlines experience an increase in induced voltage that is self-limited by the distance from the coast.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.421

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 studies0.0010.000
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 head0.204
Teacher spread0.199 · 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 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

Citations5
Published2025
Admission routes1
Has abstractyes

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