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Mass-23 nuclei in astrophysics

2015· article· en· W2488052248 on OpenAlexaff
P. R. Fraser, K. Amos, L. Canton, S. Karataglidis, J. P. Svenne, D. van der Kniff

Bibliographic record

VenueJournal of Physics Conference Series · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsWinnipeg Institute for Theoretical PhysicsUniversity of Manitoba
Fundersnot available
KeywordsPhysicsWhite dwarfNeutron starScatteringAstrophysicsNucleonStarsNuclear astrophysicsNuclear structureNuclear physicsRadiative transferQuantum mechanics

Abstract

fetched live from OpenAlex

The formation of mass-23 nuclei by radiative capture is of great interest in astrophysics. A topical problem associated with these isobars is the so-called 22 Na puzzle of ONe white dwarf novae, where the abundance of 22 Na observed is not as is predicted by current stellar models, indicating there is more to learn about how the distribution of elements in the universe occurred. Another concerns unexplained variations in elements abundance on the surface of aging red giant stars. One method for theoretically studying nuclear scattering is the Multi-Channel Algebraic Scattering (MCAS) formalism. Studies to date have used a simple collective-rotor prescription to model the target states which couple to projectile nucleons. While, in general, the target states considered all belong to the ground state rotor band, for some systems it is necessary to include coupling to states outside of this band. Herein we discuss an extension of MCAS to allow coupling of different strengths between such states and the ground state band. This consideration is essential when studying the scattering of neutrons from 22 Ne, a necessary step in studying the mass-23 nuclei mentioned above.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.149
Threshold uncertainty score0.498

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1490.048

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 head0.046
GPT teacher head0.296
Teacher spread0.249 · 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 designTheoretical or conceptual
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
Published2015
Admission routes1
Has abstractyes

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