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Record W1534709178 · doi:10.1109/ceidp.2005.1560773

A probabilistic model to evaluate characteristics of lightning channels in two-layer soils and application

2005· article· en· W1534709178 on OpenAlexaff
Zeqing Song, M.R. Raghuveer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLightning (connector)Homogeneity (statistics)HomogeneousProbabilistic logicSoil waterEnvironmental scienceCurrent (fluid)Channel (broadcasting)DiffusionGeologyGeotechnical engineeringSoil scienceComputer scienceEngineeringElectrical engineeringStatistical physicsPhysics

Abstract

fetched live from OpenAlex

Lightning-caused breakdown channels in ground may be intercepted by buried cables and suffer damage. Assessment of the possibility of such occurrences is important in the design of protection schemes and mitigating strategies. Existing models are based on diffusion of lightning current into the homogeneous soil. They do not model breakdown channel formation in ground and ignore soil non-homogeneity. In this paper a representative two-dimensional model is presented which accounts for the random growth of channels in two-layered non-homogeneous soils. The influence of lightning current magnitude, geometric configuration and soil parameters has been considered. The results obtained are in agreement with observed phenomenon. The suggested model provides a means for complete assessment of the lightning performance of buried cables.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.015
GPT teacher head0.274
Teacher spread0.259 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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