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Record W3115609501 · doi:10.1103/physrevc.103.034329

Investigation of pair-correlated <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>0</mml:mn><mml:mo>+</mml:mo></mml:msup></mml:math> states in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mi>Ba</mml:mi><mml:mprescripts/><mml:none/><mml:mn>134</mml:mn></mml:mmultiscripts></mml:math> via the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mmultiscripts><mml:mi>Ba</mml:mi><mml:mprescripts/><mml:none/><mml:mn>136</mml:mn></mml:mmultiscripts><mml:mo>(</mml:mo><mml:mi>p</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> reaction

2021· article· lv· W3115609501 on OpenAlexaff
J. C. Nzobadila Ondze, B. Rebeiro, S. Triambak, L. Atar, G. C. Ball, V. Bildstein, C. Burbadge, A. Diaz Varela, T. Faestermann, P. E. Garrett, R. Hertenberger, M. Kamil, R. Lindsay, J. N. Orce, A. J. Radich, H.‐F. Wirth

Bibliographic record

VenuePhysical review. C · 2021
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsTRIUMFUniversity of Guelph
FundersNational Research Foundation
KeywordsAlgorithmArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

We performed a high resolution study of ${0}^{+}$ states in $^{134}\mathrm{Ba}$ using the $^{136}\mathrm{Ba}(p,t)$ two-neutron transfer reaction. Our experiment shows a significant portion of the $L=0$ pair-transfer strength concentrated at excited ${0}^{+}$ levels in $^{134}\mathrm{Ba}$. Potential implications in the context of $^{136}\mathrm{Xe}\phantom{\rule{4pt}{0ex}}\ensuremath{\rightarrow}\phantom{\rule{4pt}{0ex}}^{136}\mathrm{Ba}$ neutrinoless double beta decay matrix element calculations are briefly discussed.

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)0.006
metaresearch head score (Gemma)0.048
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: none
Teacher disagreement score0.035
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0030.002
Scholarly communication0.0050.006
Open science0.0030.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0350.010

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 head0.025
GPT teacher head0.263
Teacher spread0.237 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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Same venuePhysical review. CSame topicNuclear physics research studiesFrench-language works237,207