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Record W7038453032

The IceCube-Gen2 Collaboration -- Contributions to the 38th International Cosmic Ray Conference (ICRC2023)

2023· preprint· en· W7038453032 on OpenAlexaff

Bibliographic record

VenueResearch Portal (King's College London) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsCosmic rayCOSMIC cancer databaseConjunction (astronomy)Satellite
DOInot available

Abstract

fetched live from OpenAlex

IceCube-Gen2 is a planned next-generation neutrino observatory at the South Pole that builds upon the successful design of IceCube. Integrating two complementary detection technologies for neutrinos, optical and radio Cherenkov emission, in combination with a surface array for cosmic ray air shower detection, IceCube-Gen2 will cover a broad neutrino energy range from MeV to EeV. This index of contributions to the 38th International Cosmic Ray Conference in Nagoya, Japan (July 26 - August 3, 2023) describes research and development efforts for IceCube-Gen2. Included are summaries of the design, status, and sensitivity of the IceCube-Gen2 optical, surface, and radio components; performance studies of next-generation optical sensors detecting optical Cherenkov radiation from cosmic ray and neutrino events; reconstruction techniques of radio and optical events in terms of energy, direction, and neutrino flavor; and sensitivity studies of astrophysical neutrino flavors, diffuse neutrino fluxes, and cosmic ray anisotropies. Contributions related to IceCube and the scheduled IceCube Upgrade are available in a separate collection.

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)0.008
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.057
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0060.003
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0570.030

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 head0.040
GPT teacher head0.353
Teacher spread0.313 · 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 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
Published2023
Admission routes1
Has abstractyes

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