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Record W4297582512 · doi:10.17615/pb7s-4w63

Ground-state and decay properties of neutron-rich Nb 106

2022· article· en· W4297582512 on OpenAlexfundaboutno aff
G.J. Lane, R. Orford, P. Chowdhury, R.V.F. Janssens, J.M. Allmond, S. Bottoni, Unnati Patel, Yiran Zhong, S. Zhu, A.J. Mitchell, G. Savard, A.D. Ayangeakaa, J.A. Clark, F.G. Kondev, C.J. Lister, M.L. Smith, M.P. Carpenter, P. Copp, D. Seweryniak, D.A. Gorelov

Bibliographic record

VenueUNC Libraries · 2022
Typearticle
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsnot available
FundersArgonne National LaboratoryNuclear PhysicsOffice of Defense Nuclear NonproliferationAustralian Research CouncilNatural Sciences and Engineering Research Council of CanadaNational Nuclear Security AdministrationOffice of ScienceU.S. Department of Energy
KeywordsNeutronState (computer science)Nuclear physicsPhysicsNuclear engineeringComputer scienceEngineeringProgramming language

Abstract

fetched live from OpenAlex

The ground-state properties of neutron-rich Nb106 and its β decay into Mo106 have been studied using the CARIBU radioactive-ion-beam facility at Argonne National Laboratory. Niobium-106 ions were extracted from a Cf252 fission source and mass separated before being delivered as low-energy beams to the Canadian Penning Trap, as well as the X-Array and SATURN β-decay-spectroscopy station. The measured Nb106 ground-state mass excess of -66202.0(13) keV is consistent with a recent measurement but has three times better precision; this work also rules out the existence of a second long-lived, β-decaying state in Nb106 above 5 keV in excitation energy. The decay half-life of Nb106 was measured to be 1.097(21) s, which is 8% longer than the adopted value. The level scheme of the decay progeny, Mo106, has been expanded up to ≈4MeV. The distribution of decay strength and considerable population of excited states in Mo106 of J≥3 emphasizes the need to revise the adopted Jπ=1- ground-state spin-parity assignment of Nb106; it is more likely to be J≥3.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.086
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.180
Teacher spread0.159 · 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 teacher head, 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

Citations0
Published2022
Admission routes2
Has abstractyes

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