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Light Isotope Abundances in Solar Energetic Particles Measured by the NINA-2 Instrument

2003· article· en· W17568649 on OpenAlexfundno aff
V. V. Mikhailov, A. Bakaldin, A. M. Galper, S. V. Koldashov, M. Korotkov, A. Leonov, J. Mikhaylova, S.A. Voronov, V. Bidoli, M. Casolino, Marco De Pascale, Gianluca Furano, Andrea Iannucci, A. Morselli, P. Picozza, R. Sparvoli, M. Boezio, V. Bonvicini, A. Vacchi, N. Zampa, M. Ambriola, R. Bellotti, F. Cafagna, M. Circella, C. De Marzo, O. Adriani, P. Papini, П. Спиллантини, S. Straulino, E. Vannuccini, M. Ricci, G. Castellini

Bibliographic record

VenueInternational Cosmic Ray Conference · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Education, Culture, Sports, Science and Technology
KeywordsLa NiñaPhysicsProtonDeuteriumRange (aeronautics)Solar energetic particlesIsotopeNuclear physicsAstronomyOpticsMeteorologyCoronal mass ejectionMaterials sciencePlasmaSolar wind

Abstract

fetched live from OpenAlex

The instrument NINA-2 flew on board the satellite MITA between July 2000 and August 2001, in circular polar orbit. This paper reports about a set of Solar Energetic Particle events measured by the NINA-2 instrument. The detector has mass resolution of about 0.15 amu for light nuclei and gives the possibility to observe hydrogen and helium isotopes in the energy range 10–50 MeV/n. Data of 3He and 4He were used to determine the 3He/4He ratio. For each event the deuterium-to-proton ratio was also estimated. This ratio, averaged over all events, is less than 3×10−5. 1.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.223
Teacher spread0.206 · 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 designObservational
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
Published2003
Admission routes1
Has abstractyes

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