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Record W4411824968 · doi:10.1088/0026-1394/62/1a/01002

Comparison of partial discharge (PD) calibrators from 0.1 pC to 10 nC

2025· article· en· W4411824968 on OpenAlexaff
Joni Klüss, Jari Hällström, Jussi Havunen, Wei Yan, Jan Hlávacĕk, G. Crotti, T Aguado, R Vanconcellos, N Liu, Johann Meisner, Ahmet Merev, M. Zeier, M Berginc, Grzegorz Sadkowski, Yazid Hadjadj

Bibliographic record

VenueMetrologia · 2025
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsPartial dischargeMaterials scienceNuclear engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Main text High-voltage partial discharge (PD) tests require calibration of PD measurement systems using PD calibrators which provide known values of the apparent charge. A set of travelling reference calibrators was circulated among 14 participating laboratories and each laboratory reported apparent change and rise time measured using their own procedures. Comparison reference values (CRV) were calculated for each nominal charge level and calibrator and reasonable agreement of apparent charge calibration was reached. However, agreement of rise time measurements was unexpectedly poor. The scatter in reported rise time values is not due to the instability of the travelling reference and is largely dependent on the implemented methodology. Due to the large range of variables possibly influencing reported results for rise time, the CRV for rise time is omitted. To reach the main text of this paper, click on Final Report . Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/ . The final report has been peer-reviewed and approved for publication by the CCEM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.998

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)0.0030.000

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 head0.024
GPT teacher head0.324
Teacher spread0.300 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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