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Record W2145616143 · doi:10.2174/1652803401204010060

Cn Tower Lightning Return-Stroke Current Simulation

2012· article· en· W2145616143 on OpenAlexaff
Khaled Elrodesly

Bibliographic record

VenueJournal of Lightning Research · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTowerWaveformLightning (connector)Pulse (music)Peak currentSIGNAL (programming language)AcousticsMeteorologyPhysicsElectrical engineeringControl theory (sociology)StatisticsSimulationMathematicsComputer scienceEngineeringPower (physics)VoltageStructural engineering

Abstract

fetched live from OpenAlex

Different functions have been used to model the lightning return-stroke current with the aid of direct current measurements at tall structures.In this paper, a comparison between the Pulse function and Heilder function is carried out to find out the suitability of each of these functions for simulating the lightning return-stroke current, measured at the CN Tower.An automated system for determining the CN Tower lightning return-stroke current waveform parameters is introduced.The curve fitting technique of this system and the estimation of the initial values of the simulation function parameters are presented.An artificial lightning signal, free of noise and reflections, is used as a reference signal for evaluating the parameter extraction system.Finally, cumulative statistics of the CN Tower lightning return-stroke current waveform parameters (peak, maximum rate of rise, risetime, pulse width, decay time and charge) are derived using the Pulse function parameters.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.081
GPT teacher head0.410
Teacher spread0.330 · 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 designSimulation or modeling
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

Citations8
Published2012
Admission routes1
Has abstractyes

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