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Record W2140653683 · doi:10.1109/ias.1988.25269

Effect of discharge electrode and body geometry on the relative probability and severity of the ESD event in electronic systems

2003· article· en· W2140653683 on OpenAlexaff
W.D. Greason

Bibliographic record

VenueConference Record of the 1988 IEEE Industry Applications Society Annual Meeting · 2003
Typearticle
Languageen
FieldEngineering
TopicElectrostatic Discharge in Electronics
Canadian institutionsWestern University
Fundersnot available
KeywordsElectrostatic dischargeElectrodeConductorCapacitanceEvent (particle physics)Electrical conductorElectric fieldGroundElectrical engineeringBrush dischargeDielectric barrier dischargeMechanicsPosition (finance)PhysicsGeometryMathematicsEngineeringQuantum mechanicsVoltage

Abstract

fetched live from OpenAlex

Capacitance coefficients are measured for electrostatic discharge (ESD) in a basic two-conductor system, simulating the approach of a charged object (the human body) to a second object which can be floating or grounded (the equipment under test). Parameters studied include the size of the conductors, the size and relative position of the discharge electrode, the separation between the discharge electrode and the second body, and the position of the bodies relative to a horizontal ground plane. System equations based on Maxwell's technique are solved to yield the body potentials and the electric field in the air gap between the discharge electrode and the second body. The relative probability of a discharge and severity of the discharge event are compared for the various configurations. The results not only show how the studied parameters affect the ESD event, but also provide a better understanding of the phenomenon based on the distribution of electric flux between the two bodies and ground.>

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)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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Same venueConference Record of the 1988 IEEE Industry Applications Society Annual MeetingSame topicElectrostatic Discharge in ElectronicsFrench-language works237,207