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Record W2556202344 · doi:10.3390/atoms5010014

Electroweak Decay Studies of Highly Charged Radioactive Ions with TITAN at TRIUMF

2017· article· en· W2556202344 on OpenAlexafffund
K. G. Leach, I. Dillmann, R. Klawitter, E. Leistenschneider, A. Lennarz, T. Brunner, D. Frekers, C. Andreoiu, A. A. Kwiatkowski, J. Dilling

Bibliographic record

VenueAtoms · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsSimon Fraser UniversityMcGill UniversityUniversity of British ColumbiaTRIUMF
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsPhysicsRadioactive decayIonAtomic physicsElectronNuclear physicsIonizationElectroweak interactionIsotopePenning trapParticle physics

Abstract

fetched live from OpenAlex

Several modes of electroweak radioactive decay require an interaction between the nucleus and bound electrons within the constituent atom. Thus, the probabilities of the respective decays are not only influenced by the structure of the initial and final states in the nucleus, but can also depend strongly on the atomic charge. Conditions suitable for the partial or complete ionization of these rare isotopes occur naturally in hot, dense astrophysical environments, but can also be artificially generated in the laboratory to selectively block certain radioactive decay modes. Direct experimental studies on such scenarios are extremely difficult due to the laboratory conditions required to generate and store radioactive ions at high charge states. A new electron-beam ion trap (EBIT) decay setup with the TITAN experiment at TRIUMF has successfully demonstrated such techniques for performing spectroscopy on the radioactive decay of highly charged ions.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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

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 head0.029
GPT teacher head0.329
Teacher spread0.300 · 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

Citations12
Published2017
Admission routes2
Has abstractyes

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