MétaCan
Menu
Back to cohort
Record W2182839076 · doi:10.1093/ptep/ptw034

Observation of large enhancements of charge exchange cross sections with neutron-rich carbon isotopes

2016· article· en· W2182839076 on OpenAlexaff
I. Tanihata, S. Terashima, R. Kanungo, F. Ameil, J. Atkinson, Y. Ayyad, D. Cortina‐Gil, I. Dillmann, A. Estradé, A. Evdokimov, F. Farinon, H. Geißel, G. Guastalla, R. Janik, R. Knoebel, J. Kurcewicz, Yu. A. Litvinov, M. Marta, I. Mukha, C. Nociforo, H. J. Ong, S. Piétri, A. Prochazka, C. Scheidenberger, B. Sitár, P. Strmeň, M. Takechi, Junki Tanaka, H. Toki, J. E. Ramirez Vargas, J. S. Winfield, H. Weick

Bibliographic record

VenueProgress of Theoretical and Experimental Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsIsotopePhysicsNeutronNuclear physicsIsotopes of nitrogenCarbon fibersAtomic physicsMaterials science

Abstract

fetched live from OpenAlex

Production cross sections of nitrogen isotopes from high-energy (⁠|$\sim $|950 MeV per nucleon) carbon isotopes on hydrogen and carbon targets have been measured for the first time for a wide range of isotopes (⁠|$A = 12$| to 19). The fragment separator FRS at GSI was used to deliver C-isotope beams. The cross sections of the production of N-isotopes were determined by charge measurements of forward-going fragments. The cross sections show a rapid increase with the number of neutrons in the projectile. Since the production of nitrogen is mostly due to charge-exchange (Cex) reactions below the proton separation energies, the present data suggests a concentration of Gamow–Teller and/or Fermi transition strength at low excitation energies for neutron-rich carbon isotopes. It was also observed that the Cex cross sections were enhanced much more strongly for neutron-rich isotopes in the C-target data.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.311
Teacher spread0.297 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProgress of Theoretical and Experimental PhysicsSame topicNuclear physics research studiesFrench-language works237,207