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Record W4388221616 · doi:10.3390/en16217406

Review of Geomagnetically Induced Current Proxies in Mid-Latitude European Countries

2023· article· en· W4388221616 on OpenAlexaboutno aff
Agnieszka Gil, Monika Berendt-Marchel, Renata Modzelewska, Agnieszka Siluszyk, Marek Siłuszyk, Anna Wawrzaszek, Anna Wawrzynczak

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGeomagnetically induced currentBlackoutLatitudeElectric power transmissionPower transmissionMeteorologyGeographyClimatologyGeologyPower (physics)Electric power systemGeomagnetic stormEarth's magnetic fieldEngineeringGeodesy

Abstract

fetched live from OpenAlex

The Quebec blackout on 13 March 1989, has made geomagnetically induced current (GIC) research a socially important field of study. It is widely recognized that the effects of space weather, which may affect the power infrastructure, threaten countries located at high latitudes. However, in recent years, various studies have shown that countries at lower latitudes may also be at risk. One of the best proxies of GIC variability is the local geoelectric field, as measured in Eskdalemuir, Lerwick, and Hartland, by the British Geological Survey or modeled using, e.g., a 1D layered Earth conductivity model. In our article, we present a review of the issues related to the impact of the GIC on transformers and transmission lines in Central and Southern European countries, from Greece, Spain, and Italy to Slovakia, the Czech Republic, Austria, and Poland. The review underlines the importance of the systematic collection of information about power grid failures and the need for further systematic studies of the GIC’s impact on the operation of power grids in mid-latitude countries.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.012
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations18
Published2023
Admission routes1
Has abstractyes

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