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Record W4383893105 · doi:10.2172/1988450

Scale Tests of the New DUNE Data Pipeline

2023· article· en· W4383893105 on OpenAlexfundno aff
S. Timm, Wenlong Yuan, Douglas Benjamin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsnot available
FundersHigh Energy PhysicsHorizon 2020 Framework ProgrammeInstitut National de Physique Nucléaire et de Physique des ParticulesScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaOffice of ScienceEuropean CommissionMinisterio de Ciencia e InnovaciónCentre National de la Recherche ScientifiqueFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroConselho Nacional de Desenvolvimento Científico e TecnológicoEuropean Regional Development FundU.S. Department of EnergyJunta de AndalucíaFundação para a Ciência e a TecnologiaFundação de Amparo à Pesquisa do Estado de GoiásFermilabUK Research and InnovationNational Science FoundationRoyal SocietyNational Energy Research Scientific Computing CenterXunta de GaliciaCERNFundação de Amparo à Pesquisa do Estado de São PauloSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsScale (ratio)Computer sciencePipeline (software)GeologyCartographyProgramming languageGeography

Abstract

fetched live from OpenAlex

\nIn preparation for the second runs of the ProtoDUNE detectors at CERN (NP02 and NP04)[1], DUNE has established a new data pipeline for bringing the data from the EHN-1 experimental hall at CERN to primary tape storage at Fermilab and CERN, and then spreading it out to a distributed disk data store at many locations around the world. This system includes a new Ingest Daemon and a new Declaration Daemon. The Rucio[2] replica catalog, and FTS3 transport are used to transport all files. All file metadata is declared to the new MetaCat[3] metadata service. All of these new components have been successfully tested at a scale equal to the expected output of the detector data acquisition system (~2-4 GB/s), and the expected network bandwidth out of the experimental hall. We present the procedure that was used to test and the results of the test.\n

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.134

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 head0.026
GPT teacher head0.260
Teacher spread0.234 · 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 designNot applicable
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

Citations1
Published2023
Admission routes1
Has abstractyes

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