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Record W2155159702 · doi:10.1109/23.914454

High-voltage microdischarge in ultra-low background /sup 3/He proportional counters

2000· article· en· W2155159702 on OpenAlexaboutno aff
K. M. Heeger, S. R. Elliott, R. G. H. Robertson, M.W.E. Smith, J. F. Wilkerson

Bibliographic record

VenueIEEE Transactions on Nuclear Science · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsnot available
FundersLos Alamos National LaboratoryUniversity of Washington
KeywordsPhysicsVoltageDetectorLow voltageProportional counterNeutrinoHigh voltageOpticsOptoelectronicsNuclear physics

Abstract

fetched live from OpenAlex

This paper discusses the phenomenon of surface microdischarge induced by high voltage, and techniques for the reduction and discrimination of such breakdowns in ultralow background proportional counters. An array of ultra-low background /sup 3/He-filled proportional counters will measure the neutral-current interaction rate of all active neutrino species in the Sudbury Neutrino Observatory. The sensitivity of these neutral current detectors and their stringent background criteria make it essential to minimize all spurious pulses including signals induced by high voltage. Such pulses can originate from microscopic surface discharges in the various proportional counter components. Studies have shown that this discharge effect occurs mainly at interfaces and in microscopic voids between dielectric surfaces, on contaminated surfaces, and on surfaces with imperfections. Because of its occurrence in various materials and environments, microdischarge is a concern for all low-background detectors that operate under high voltage.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.262
Teacher spread0.250 · 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 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

Citations15
Published2000
Admission routes1
Has abstractyes

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