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Record W2152031751 · doi:10.1109/nssmic.2000.949978

A control and data acquisition system for a large volume superheated droplet detector

2002· article· en· W2152031751 on OpenAlex
R. Gornea, N. Boukhira, I. Boussaroque, L. Lessard, M. Di Marco, J.P. Martin, Johanne Vinet, V. Zacek

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.

Bibliographic record

Venue2000 IEEE Nuclear Science Symposium. Conference Record (Cat. No.00CH37149) · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsDetectorWeakly interacting massive particlesPhysicsData acquisitionModular designVolume (thermodynamics)Dark matterSuperheatingSensitivity (control systems)Nuclear physicsParticle physicsOpticsComputer scienceElectronic engineeringEngineeringAstrophysics

Abstract

fetched live from OpenAlex

Large-volume room-temperature superheated droplet detectors are being constructed for measuring very weakly interacting radiation fields, such as those produced by Cold Dark Matter particles (CDM particles, or Weakly Interacting Massive Particles: WIMPS), and various versions of Control and Data Acquisition systems (CDAQ) have been developed for such detectors. Large active mass droplet detectors are modular, their sensitivity is strongly temperature dependent and their operation requires measurements and control functions which are unique to this particular detection medium. We present the CDAQ systems developed for the PICASSO project for different levels of operation. Other types of applications of such detectors are also being investigated and appear promising.

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)0.001
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.016
GPT teacher head0.231
Teacher spread0.214 · 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