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Record W1540557790 · doi:10.1051/epjconf/20159504020

PICASSO, COUPP and PICO - search for dark matter with bubble chambers

2015· article· en· W1540557790 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEPJ Web of Conferences · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsUniversity of AlbertaUniversité de MontréalLaurentian UniversityUniversity of TorontoSnolabQueen's University
FundersPacific Northwest National LaboratoryNatural Sciences and Engineering Research Council of CanadaOffice of ScienceNational Science FoundationMinisterio de Economía y CompetitividadBattelleFermilabHigh Energy PhysicsU.S. Department of EnergyUniversity of Chicago
KeywordsPhysicsDark matterWeakly interacting massive particlesNuclear physicsDetectorParticle physicsSuperheatingBubble chamberCold dark matterAstrophysicsOpticsCosmologyDark energyScalar field dark matter

Abstract

fetched live from OpenAlex

The PICASSO and COUPP collaborations use superheated liquid detectors to search for cold dark matter through the direct detection of weakly interacting massive particles (WIMPs). These experiments, located in the underground laboratory of SNOLAB, Canada, detect phase transitions triggered by nuclear recoils in the keV range induced by interactions with WIMPs. We present details of the construction and operation of these detectors as well as the results, obtained by several years of observations. We also introduce PICO, a joint effort of the two collaborations to build a second generation ton-scale bubble chamber with 250 liters of active liquid.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.298

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)0.0000.000

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 head0.025
GPT teacher head0.257
Teacher spread0.232 · 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