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Record W4385845941 · doi:10.21468/scipost.report.6087

Report on scipost_202210_00002v1

2022· peer-review· en· W4385845941 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsnot available
FundersLabex UnivEarthSCentro de Investigaciones Energéticas, Medioambientales y TecnológicasMinisterio de Ciencia e InnovaciónConselho Nacional de Desenvolvimento Científico e TecnológicoScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaIstituto Nazionale di Fisica NucleareEuropean Regional Development FundFundacja na rzecz Nauki PolskiejFundação de Amparo à Pesquisa do Estado de São PauloRussian Science FoundationNational Science FoundationRoyal SocietyAgence Nationale de la Recherche
KeywordsComputer science

Abstract

fetched live from OpenAlex

DarkSide-20k (DS-20k) will exploit the physical and chemical properties of liquid argon housed within a large dual-phase time project chamber (TPC) in its direct search for dark matter.The TPC will utilize a compact, integrated design with many novel features to enable the 20 t fiducial volume of underground argon.Underground Argon (UAr) is sourced from underground CO 2 wells and depleted in the radioactive isotope 39 Ar, greatly enhancing the experimental sensitivity to dark matter interactions.Sourcing and transporting the O(100 t) of UAr for DS-20k is costly, and a dedicated single-closedloop cryogenic system has been designed, constructed, and tested to handle the valuable UAr.We present an overview of the DS-20k TPC design and the first results from the UAr cryogenic system.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.887
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.8870.792

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 head0.026
GPT teacher head0.367
Teacher spread0.341 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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