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Record W1994312202 · doi:10.1063/1.3292334

SuperCDMS Detector Readout Cryogenic Hardware

2009· article· lt· W1994312202 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAIP conference proceedings · 2009
Typearticle
Languagelt
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of EnergySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsIconCitationComputer graphics (images)Computer scienceInformation retrievalHumanitiesWorld Wide WebPhysicsLibrary scienceCombinatoricsArtProgramming languageMathematics

Abstract

fetched live from OpenAlex

SuperCDMS employs 1-inch thick gennanium crystals operated below 50mK in a dilution cryostat. Each detector produces ionization and phonon signals. Ionization signals are amplified by JFETs operating at 150K within an assembly mounted on the 4K cryostat stage. These high impedance signals are carried to the FETs by superconducting "vacuum coaxes" which minimize thermal conductivity, stray capacitance, and microphonics. Transition edge sensors produce low-impedance phonon signals, amplified by SQUID arrays mounted on a 600mK stage. Detectors are mounted in a six-sided wiring configuration called a "tower", which carries signals from 40mK to 4K. A flex circuit 3 meters in length carries amplified signals for each detector from 4K to a vacuum bulkhead. We describe the methods used to support the detectors, wiring and amplifier elements at various thermal stages, minimizing electrical noise and thermal loads.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.025
GPT teacher head0.248
Teacher spread0.222 · 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