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Record W2152690027 · doi:10.1109/tpwrd.2005.860264

Onsite MOSA Condition Assessment—A New Approach

2006· article· en· W2152690027 on OpenAlexaff
C. Karawita, M.R. Raghuveer

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsUniversity of Manitoba
FundersUniversity of Moratuwa
KeywordsSurge arresterResistive touchscreenReliability (semiconductor)Current (fluid)Lightning arresterElectrical engineeringComponent (thermodynamics)EngineeringReliability engineeringElectronic engineeringCapacitive sensingPower (physics)Physics

Abstract

fetched live from OpenAlex

A new method to assess the condition of metal-oxide surge arresters is presented. The fundamental component of the resistive current is used as an indicator. This current is obtained by exploiting the linear relationship, which exists between the peak value of the fundamental component of the resistive current and the phase shift between the fundamental components of the capacitive current and the total arrester current. This relationship is independent of temperature for a given type of arrester. Results are presented to demonstrate the accuracy and the reliability of the new technique.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.812
Threshold uncertainty score0.999

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)0.0020.000

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 head0.007
GPT teacher head0.222
Teacher spread0.216 · 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 designBench or experimental
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

Citations16
Published2006
Admission routes1
Has abstractyes

Explore more

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