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Spectroscopic Study of <sup>39</sup>Ca for Endpoint Nucleosynthesis in Classical Novae

2020· article· en· W3093635529 on OpenAlexaff
J. F. Liang, A. A. Chen, Marius Anger, S. Bishop, T. Faestermann, C. Fry, R. Hertenberger, A. Psaltis, D. Seiler, P. C. Tiwari, H. F. Wirth, C. Wrede

Bibliographic record

VenueJournal of Physics Conference Series · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNucleosynthesisNuclideNova (rocket)PhysicsNuclear physicsReaction rateAstrophysicsNuclear reactionChemistry

Abstract

fetched live from OpenAlex

Abstract Classical novae are environments which can produce heavier elements up to mass A ∼ 40. These nuclides at the endpoint of nova nucleosynthesis consist of elements such as Ar, K, and Ca. There is an order of magnitude discrepancy with the predicted and theoretical abundances of these endpoint nuclides produced in a classical nova. The uncertainty in the theoretical 38K(p,γ)39Ca reaction rate has been shown to affect the abundances by an order of magnitude or more. The only direct measurement of this reaction rate was performed with the DRAGON facility at TRIUMF; however additional spectroscopic data could aid the interpretation of this data as well as motivate further study of this reaction rate. In this study, we present the preliminary results of a spectroscopic study of 39Ca using the 40Ca(d,t)39Ca reaction carried out at the Maier-Leibnitz Laboratory in Garching, Germany.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.046
GPT teacher head0.302
Teacher spread0.256 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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