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Record W7097147425

POWER AND PIPELINES (GROUND SYSTEMS)

2014· article· en· W7097147425 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeomagnetically induced currentPipeline transportElectric powerElectric power transmissionElectric power systemElectric fieldPower transmissionCathodic protectionPower (physics)
DOInot available

Abstract

fetched live from OpenAlex

Geomagnetically induced currents (GIC) in technological sys-tems, such as electric power transmission systems, oil and gas pipelines, telecommunication cables and railway equipment, are a manifestation of space weather at the earth's surface. In power systems, GIC cause saturation of transformers, which may lead to problems in the operation of the system, and even to a collapse of the whole system and to permanent damages of transformers. The best-known GIC effect occurred in March 1989 when the Québec province in Canada suffered from an electric black-out for about nine hours. The corrosion rate may be increased in pipelines when GIC flows from the pipe into the soil, and the associated voltages can disturb pipeline sur-veys and the cathodic protection. This paper summarizes GIC effects on power systems and pipelines. GIC research associ-ated with the Finnish high-voltage power system is discussed. GIC measurement data on a natural gas pipeline and Sweden is presented. The electric field observed at the earth’s surface during a geomagnetic disturbance is the key quantity for the calculation of GIC magnitudes. It depends on currents in the ionosphere and on currents flowing within the earth. The theo-retical modelling of the electric field is discussed in this paper. Particular attention is paid to the complex image method, which permits accurate and fast computations of the electric field and which is thus suitable for time-critical applications like GIC forecasting. 1.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.998

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)0.0030.000

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 head0.007
GPT teacher head0.185
Teacher spread0.178 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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