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Record W2974339482 · doi:10.1051/epjconf/201921403058

IceProd - a dataset management system for IceCube: update

2019· article· en· W2974339482 on OpenAlexfundno aff
David Schultz

Bibliographic record

VenueEPJ Web of Conferences · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
FundersOffice of Polar ProgramsBundesministerium für Bildung und ForschungMarsden FundJapan Society for the Promotion of ScienceU.S. Department of EnergyUniversity of Wisconsin-MadisonVetenskapsrådetNatural Sciences and Engineering Research Council of CanadaKnut och Alice Wallenbergs StiftelseNational Research Foundation of KoreaFonds Wetenschappelijk OnderzoekHelmholtz Alliance for Astroparticle PhysicsDanmarks GrundforskningsfondNational Science FoundationBelgian Federal Science Policy OfficeDeutsche ForschungsgemeinschaftNational Research FoundationWestern Canada Research GridVlaamse regeringUniversity of OxfordCompute CanadaFonds De La Recherche Scientifique - FNRSPolarforskningssekretariatetSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsPython (programming language)ScalabilityCodebaseComputer scienceNeutrinoTrack (disk drive)Operating systemPhysicsSoftwareParticle physics

Abstract

fetched live from OpenAlex

IceCube is a cubic kilometer neutrino detector located at the south pole. IceProd is IceCube’s internal dataset management system, keeping track of where, when, and how jobs run. It schedules jobs from submitted datasets to HTCondor, keeping track of them at every stage of the lifecycle. Many updates have happened in the last years to improve stability and scalability, as well as increase user access. Along the way, the IceProd codebase switched from Python 2 to Python 3.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.910
Threshold uncertainty score0.437

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 head0.011
GPT teacher head0.233
Teacher spread0.221 · 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 designTheoretical or conceptual
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

Citations0
Published2019
Admission routes1
Has abstractyes

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