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Measuring neutron capture cross sections of radioactive nuclei

2023· article· en· W4376277959 on OpenAlexafffundabout
I. Dillmann, O. Kester, R. Baartman, Alan Chen, Tobias Junginger, Falk Herwig, D. Kaltchev, A. Lennarz, Thomas Planche, C. Ruiz, Nicole Vassh

Bibliographic record

VenueThe European Physical Journal A · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsUniversity of VictoriaMcMaster UniversityTRIUMF
FundersNatural Sciences and Engineering Research Council of CanadaTRIUMF
KeywordsNuclear physicsNeutronPhysicsNeutron captureIsotopeRange (aeronautics)Neutron sourceRadiochemistryChemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract Measuring neutron capture cross sections of radioactive nuclei is a crucial step towards a better understanding of the origin of the elements heavier than iron. For decades, the precise measurement of direct neutron capture cross sections in the “stellar” energy range (eV up to a few MeV) was limited to stable and longer-lived nuclei that could be provided as physical samples and then irradiated with neutrons. New experimental methods are now being developed to extend these direct measurements towards shorter-lived radioactive nuclei ( $$t_{1/2}<$$ t 1 / 2 < 1 y). One project in this direction is a low-energy heavy-ion storage ring coupled to the ISAC facility at TRIUMF, Canada’s accelerator laboratory in Vancouver BC, which has a compact neutron source in the ring matrix. Such a pioneering facility could be built within the next 10 years and store a wide range of radioactive ions provided directly from the existing ISOL facility, allowing for the first time to carry out direct neutron capture measurements on short-lived isotopes in inverse kinematics.

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)0.001
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.038
GPT teacher head0.297
Teacher spread0.258 · 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

Citations9
Published2023
Admission routes3
Has abstractyes

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