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Record W2332752569 · doi:10.1109/iclp.2014.6973337

Shielding failure evaluation by collection surface

2014· article· en· W2332752569 on OpenAlexaff
Qizhang Xie, Stéphane Baron, Simon Fortin, Sylvie Lefebvre, F. Dawalibi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsSafe Engineering Services & Technologies (Canada)
Fundersnot available
KeywordsLightning (connector)Electromagnetic shieldingData collectionSurface (topology)Computer scienceAmplitudeGraphicsElectrical engineeringComputer graphics (images)EngineeringGeometryPower (physics)MathematicsOpticsStatisticsPhysics

Abstract

fetched live from OpenAlex

A new lightning shielding failure evaluation method based on an electro-geometric method aided by 3D graphics technology is introduced. The approach is based on the method of collection surfaces. By using the collection surface of protected equipment instead of the unprotected area, empty areas where no equipment is to be protected are eliminated from contributing to the failure rate. Shielding devices and protected equipment are considered equally as targets for lightning when generating collection surfaces. The collection surfaces are projected to a 2D bitmap, using different colors to represent protected and unprotected areas. An integral is performed on the amplitude of the stroke current (weighted by the probability distribution of this amplitude) to account for the dependency of the shape and size of the unprotected surface on the stroke current. The technique applies to structures or substations of any shape, and can use different striking distances to horizontal and vertical objects. The 69 kV substation example described in IEEE Standard 998 is used to demonstrate usage of this method.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.217
Teacher spread0.212 · 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 designNot applicable
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

Citations3
Published2014
Admission routes1
Has abstractyes

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