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Record W2168799827 · doi:10.1109/tns.2004.836148

Sudbury neutrino observatory neutral current detectors signal readout system

2004· article· en· W2168799827 on OpenAlexaboutno aff
G. A. Cox, C. A. Duba, M. A. Howe, S. McGee, A.W. Myers, K. Rielage, R. G. H. Robertson, L. C. Stonehill, B. L. Wall, J. F. Wilkerson, T.D. Van Wechel

Bibliographic record

VenueIEEE Transactions on Nuclear Science · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsnot available
FundersUniversity of Washington
KeywordsPhysicsNeutrinoNeutrino detectorSolar neutrinoParticle identificationNuclear physicsOscilloscopeDetectorFlux (metallurgy)ObservatoryNeutrino oscillationOpticsAstrophysics

Abstract

fetched live from OpenAlex

Two precise measurements of the /sup 8/B solar neutrino flux have been made at the Sudbury Neutrino Observatory (SNO) and a third measurement will be made with an array of neutral current detectors (NCD). The NCDs are /sup 3/He proportional counters which detect thermalized neutrons liberated by the neutral current reaction /spl nu//sub x/+d/spl rarr//spl nu//sub x/+n+p in SNO's D/sub 2/O. Due to the very low rate of neutrino interactions relative to the rates of other ionizing particles, one major criteria of the array readout system is to facilitate particle identification for the measurement of the solar neutrino flux. Data acquisition at a rate expected to be produced by the neutrino flux from supernovae within our galaxy (10 kpc) is another major requirement. To accomplish these two tasks, a readout system was constructed based upon two distinct pieces of hardware. Digital oscilloscopes are used to maximize particle identification, but are only capable of handling the expected data rate produced by solar neutrinos. Custom-designed VME-based shaping-discriminating-ADC boards measure the total charge of events and are capable of being read out at 20 kHz.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.278
Teacher spread0.251 · 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.

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

Citations7
Published2004
Admission routes1
Has abstractyes

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