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Record W5966323 · doi:10.22323/1.028.0171

The influence of electron screening on half-lifes

2010· article· en· W5966323 on OpenAlexaff
G. Ruprecht, L. Buchmann, D.A. Hutcheon, D. Ottewell, Camilo Ruíz, P. Walden, C Vokenhuber, K. Czerski

Bibliographic record

VenueProceedings of International Symposium on Nuclear Astrophysics - Nuclei in the Cosmos - IX — PoS(NIC-IX) · 2010
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsTRIUMF
Fundersnot available
KeywordsElectronComputer scienceMaterials sciencePhysicsNuclear physics

Abstract

fetched live from OpenAlex

Electron screening strongly changes nuclear reaction cross sections at energies below 1000 times the screening energy U e .It has been found that U e can be one order of magnitude larger than predicted by theory [1] if the target atoms are hosted in a metallic environment.As a consequence, a change of lifetimes of low-energy β and α emitters may also be considered if they are situated in a metal.In addition, a temperature dependence of the screening effect has been proposed [2], dramatically changing some half-lifes if the metal is cooled [3,5].We checked these claims experimentally by measuring the decay rate of 22 Na in a piece of aluminum activated by a 70 MeV proton beam.We observed the 22 Na activity both at room temperature and when cooled down to nearly LN 2 temperatures.As a result, a 10% increase as proposed by [3] can be clearly excluded.Furthermore, a 1/ √ T temperature dependence of U e as it was proposed by [3,5] when the Debye-Hückel model is applied is unlikely.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.256
Teacher spread0.249 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

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