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Citizen science for IceCube: Name that Neutrino

2024· article· en· W4399763770 on OpenAlexafffund
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Hermannsgabner, Lars Philipp Heuermann, N. Heyer, S. Hickford, Attila Hidvégi, Colton Hill, G. C. Hill, K. D. Hoffman, Sam Hori, K. Hoshina, Wenjie Hou, T. Huber, K. Hultqvist, M. Hünnefeld, R. Hussain, Karolin Hymon, A. Ishihara, W. Iwakiri, M. Jacquart, Oliver Janik, M. Jansson, G. S. Japaridze, Minjin Jeong, Miaochen Jin, B. J. P. Jones, N. Kamp, Donghwa Kang, Woosik Kang, X. Kang, A. Kappes, David Kappesser, Leonora Kardum, T. Karg, M. Karl, A. Karle, Akanksha Katil, U. Katz, M. Kauer, J. L. Kelley, Manish Khanal, A. Khatee Zathul, Ali Kheirandish, J. Kiryluk, Alina Kochocki, R. Koirala, H. Kolanoski, T. Kontrimas, L. Köpke, C. Kopper, D. J. Koskinen, P. Koundal, M. Kovacevich, M. Kowalski, T. Kozynets, J. Krishnamoorthi, K. Kruiswijk, E. Krupczak, Anil Kumar, Emma Kun, N. Kurahashi, N. N. Lad, Cristina Lagunas Gualda, M. Lamoureux, M. J. Larson, Silvia Latseva, Frederik Hermann Lauber, Jeffrey Lazar, J. W. 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Bibliographic record

VenueThe European Physical Journal Plus · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsUniversity of AlbertaQueen's University
FundersOffice of Experimental Program to Stimulate Competitive ResearchJapan Society for the Promotion of ScienceDeutsches Elektronen-SynchrotronNatural Sciences and Engineering Research Council of CanadaHelmholtz Alliance for Astroparticle PhysicsInstitute for Global Prominent Research, Chiba UniversityRWTH Aachen UniversityVillum FondenNational Research Foundation of KoreaMarsden FundBundesministerium für Bildung und ForschungSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science FoundationBelgian Federal Science Policy OfficeDeutsche ForschungsgemeinschaftUniversity of Wisconsin-MadisonVetenskapsrådetU.S. Department of EnergyOffice of Advanced CyberinfrastructureEuropean CommissionWestern Canada Research GridFonds De La Recherche Scientifique - FNRSPolarforskningssekretariatetAlliance de recherche numérique du CanadaChiba UniversityKnut och Alice Wallenbergs StiftelseOffice of Polar ProgramsCollege of Engineering, Michigan State UniversityAlfred P. Sloan FoundationMarquette UniversityNational Research FoundationMichigan State UniversityFonds Wetenschappelijk OnderzoekNvidia
KeywordsNeutrinoPhysicsObservatoryNeutrino detectorNeutrino astronomyArtificial neural networkParticle physicsSolar neutrino problemDetectorNeutrino oscillationComputer scienceSolar neutrinoAstronomyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Name that Neutrino is a citizen science project where volunteers aid in classification of events for the IceCube Neutrino Observatory, an immense particle detector at the geographic South Pole. From March 2023 to September 2023, volunteers did classifications of videos produced from simulated data of both neutrino signal and background interactions. Name that Neutrino obtained more than 128,000 classifications by over 1800 registered volunteers that were compared to results obtained by a deep neural network machine-learning algorithm. Possible improvements for both Name that Neutrino and the deep neural network are discussed.

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)0.001
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: none
Teacher disagreement score0.714
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.018
GPT teacher head0.257
Teacher spread0.239 · 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".

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Citations1
Published2024
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Has abstractyes

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