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Record W2329849962

Protecting the UK power grid from geomagnetic hazard

2003· article· en· W2329849962 on OpenAlexaboutno aff
Allan McKay, Sarah Reay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEarth's magnetic fieldGeomagnetic stormElectric power systemSpace weatherMeteorologyStormNatural hazardWind powerWarning systemEnvironmental sciencePower transmissionGridPower (physics)GeophysicsEngineeringGeologyElectrical engineeringTelecommunicationsGeographyMagnetic fieldGeodesyPhysics
DOInot available

Abstract

fetched live from OpenAlex

Geomagnetic storms, during which the Earth's magnetic field is disturbed, cause the beautiful natural phenomenon of the aurora borealis, or 'northern lights', but they also pose a threat to technological systems such as power transmission networks. For example, during the major geomagnetic storm of March 1989 the Canadian Hydro Quebec power system failed. This power outage lasted for nine hours and affected six million people with damage and losses estimated at hundreds of millions of dollars. This massive disruption was due to equipment failure caused by induced electrical currents flowing in the power grid as a direct consequence of a severe geomagnetic disturbance. Operational mitigation is considered one of the best strategies to minimise this kind of risk. This poster describes recent developments towards a new, near-real-time, geomagnetic storm warning system based on spacecraft measurements of the solar wind, British Geological Survey (BGS) magnetic field measurements, and an integrated Earth-surface electric field and power grid network model. This system is being developed for use by Scottish Power plc, with support of the European Space Agency (ESA/ESTEC) to aid grid control management.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.999

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)0.0800.002

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 head0.009
GPT teacher head0.179
Teacher spread0.170 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2003
Admission routes1
Has abstractyes

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