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Record W4385874973 · doi:10.22323/1.444.0118

Cosmic-ray Isotope Measurements with HELIX

2023· article· en· W4385874973 on OpenAlexafffund
Scott Wakely, P. Allison, Melissa Baiocchi, J. J. Beatty, L. Beaufore, D.H. Calderón, A. Castaño, Yu Chen, S. Coutu, Noah Green, D. Hanna, Hyebin Jeon, Susan Blakeley Klein, Brandon Kunkler, Mike Lang, Rostom Mbarek, Keith McBride, Isaac Mognet, J. Musser, Scott Nutter, S. O’Brien, Nahee Park, Kate M. Powledge, Kenichi Sakai, M. Tabata, G. Tarlé, Julia M. Tuttle, G. Visser, Scott P. Wakely, Monong Yu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsMcGill UniversityQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyNuclear Safety and Security CommissionNational Aeronautics and Space Administration
KeywordsCosmic rayPhysicsCherenkov radiationDetectorHelix (gastropod)Tracking (education)Superconducting magnetNuclear physicsOpticsMagnetGeology

Abstract

fetched live from OpenAlex

HELIX (High Energy Light Isotope eXperiment) is a balloon-borne experiment designed to measure the chemical and isotopic abundances of light cosmic-ray nuclei. Detailed measurements by HELIX, especially of $^{10}$Be from $\sim$0.2 GeV/n to beyond $\sim$3 GeV/n, will provide an essential set of data for the study of propagation processes of the cosmic rays. HELIX consists of a 1 Tesla superconducting magnet with a high-resolution gas tracking system, time-of-flight detector, and a ring-imaging Cherenkov detector. The instrument's first long-duration balloon flight is anticipated to occur in 2024. In this paper, we will briefly discuss the scientific goals of the instrument and report on its design and current status.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.238
Teacher spread0.214 · 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 designBench or experimental
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

Citations8
Published2023
Admission routes2
Has abstractyes

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