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Consistent analysis of one-nucleon spectroscopic factors involving weakly- and strongly-bound nucleons

2016· article· en· W2265904940 on OpenAlexfundno aff
J. Okołowicz, Y. H. Lam, M. Płoszajczak, A. O. Macchiavelli, N. A. Smirnova

Bibliographic record

VenuePhysics Letters B · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryCentre National de la Recherche ScientifiqueChina Postdoctoral Science FoundationMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of ChinaCommunities Foundation of TexasInstitut National de Physique Nucléaire et de Physique des ParticulesProvidence Health CareU.S. Department of Energy
KeywordsPhysicsNucleonNuclear physicsParticle physics

Abstract

fetched live from OpenAlex

There is a considerable interest in understanding the dependence of one-nucleon removal cross sections on the asymmetry of the neutron S n and proton S p separation energies, following a large amount of experimental data and theoretical analyses in a framework of sudden and eikonal approximations of the reaction dynamics. These theoretical calculations involve both the single-particle cross section and the shell-model description of the projectile initial state and final states of the reaction residues. The configuration mixing in shell-model description of nuclear states depends on the proximity of one-nucleon decay threshold but does it depend sensitively on S n − S p ? To answer this question, we use the shell model embedded in the continuum to investigate the dependence of one-nucleon spectroscopic factors on the asymmetry of S n and S p for mirror nuclei 24 Si, 24 Ne and 28 S, 28 Mg and for a series of neon isotopes ( 20 ≤ A ≤ 28 ).

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.028
GPT teacher head0.271
Teacher spread0.243 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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Same venuePhysics Letters BSame topicNuclear physics research studiesFrench-language works237,207