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Record W6894164145 · doi:10.5445/ir/1000156224

Graph Neural Networks for low-energy event classification & reconstruction in IceCube

2022· article· en· W6894164145 on OpenAlexfundno aff

Bibliographic record

VenueRepository KITopen (Karlsruhe Institute of Technology) · 2022
Typearticle
Languageen
FieldComputer Science
TopicAge of Information Optimization
Canadian institutionsnot available
FundersOffice of Experimental Program to Stimulate Competitive ResearchJapan Society for the Promotion of ScienceDeutsches Elektronen-SynchrotronOffice of Polar ProgramsCollege of Engineering, Michigan State UniversityHelmholtz Alliance for Astroparticle PhysicsInstitute for Global Prominent Research, Chiba UniversityRWTH Aachen UniversityChiba UniversityKnut och Alice Wallenbergs StiftelseVillum FondenNational Research Foundation of KoreaFonds Wetenschappelijk OnderzoekMarsden FundBundesministerium für Bildung und ForschungSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science FoundationBelgian Federal Science Policy OfficeDeutsche ForschungsgemeinschaftMichigan State UniversityNational Research FoundationVetenskapsrådetU.S. Department of EnergyEuropean CommissionWestern Canada Research GridUniversity of OxfordCompute CanadaMarquette UniversityFonds De La Recherche Scientifique - FNRSPolarforskningssekretariatet
KeywordsEvent (particle physics)Artificial neural networkGraphPattern recognition (psychology)Feature (linguistics)

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2022
Admission routes1
Has abstractno

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