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Record W4214710255 · doi:10.1051/epjconf/202226003003

Experimental studies on astrophysical reactions at the low-energy RI beam separator CRIB

2022· article· en· W4214710255 on OpenAlexaff
H. Yamaguchi, S. Hayakawa, Nan-Ru Ma, Hideki Shimizu, K. Okawa, L. Yang, D. Kahl, M. La Cognata, L. Lamia, K. Abe, O. Beliuskina, S.M. Cha, K. Y. Chae, S. Cherubini, P. Figuera, Z. W. Ge, M. Gulino, Jun Hu, A. Inoue, N. Iwasa, A. Kim, D. Kim, G. Kiss, S. Kubono, M. La Commara, М. Латтуада, Eunji Lee, J. Y. Moon, S. Palmerini, C. Parascandolo, S.Y. Park, V. H. Phong, D. Pierroutsakou, R. G. Pizzone, G. G. Rapisarda, S. Romano, C. Spitaleri, Xiaodong Tang, O. Trippella, А. Туміно, N.T. Zhang, Y. H. Lam, Alexander Heger, Adam Michael Jacobs, Shaohang Xu, S.B. Ma, L. H. Ru, Enqiang Liu, Tong Liu, C. B. Hamill, A. St. J. Murphy, Jun Su, Xiao Fang, M. S. Kwag, N.N. Duy, Nguyen Kim Uyen, D.H. Kim, J. F. Liang, A. Psaltis, Michele Sferrazza, Zac Johnston, Y.Y. Li

Bibliographic record

VenueEPJ Web of Conferences · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhysicsNuclear physicsSeparator (oil production)NucleosynthesisNuclear astrophysicsNuclear reactionIsotope

Abstract

fetched live from OpenAlex

Experimental studies on astrophysical reactions involving radioactive isotopes (RI) often accompany technical challenges. Studies on such nuclear reactions have been conducted at the low-energy RI beam separator CRIB, operated by Center for Nuclear Study, the University of Tokyo. We discuss two cases of astrophysical reaction studies at CRIB; one is for the 7Be+n reactions which may affect the primordial 7Li abundance in the Big-Bang nucleosynthesis, and the other is for the 22Mg(α, p) reaction relevantin X-raybursts.

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)0.003
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 score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.005

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 head0.038
GPT teacher head0.333
Teacher spread0.296 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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