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Record W1991955227 · doi:10.1063/1.4833926

Ion source developments for the production of radioactive isotope beams at TRIUMF

2013· article· en· W1991955227 on OpenAlexaff
F. Ames, P. Bricault, H. Heggen, P. Kunz, J. Lassen, Anders Mjøs, S. Raeder, A. Teigelhöfer

Bibliographic record

VenueReview of Scientific Instruments · 2013
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsTRIUMF
Fundersnot available
KeywordsIon sourceIonIonizationAtomic physicsIon beam depositionIon beamMaterials scienceIsotope separationIon gunBeam (structure)IsotopeNuclear physicsPhysicsOptics

Abstract

fetched live from OpenAlex

At the ISAC facility at TRIUMF radioactive ions are produced by bombarding solid targets with up to 100 μA of 500 MeV protons. The reaction products have to diffuse out of the hot target into an ion source. Normally, singly charged ions are extracted. They can be transported either directly to experiments or via an ECR charge state breeder to a post accelerator. Several different types of ion sources have to be used in order to deliver a large variety of rare isotope beams. At ISAC those are surface ion sources, forced electron beam arc discharge (FEBIAD) ion sources and resonant laser ionization sources. Recent development activities concentrated on increasing the selectivity for the ionization to suppress isobaric contamination in the beam. Therefore, a surface ion rejecting resonant laser ionization source (SIRLIS) has been developed to suppress ions from surface ionization. For the FEBIAD ion source a cold transfer line has been introduced to prevent less volatile components from reaching the ion source.

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)0.003
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.999
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.006

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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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
GenreMethods

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

Citations32
Published2013
Admission routes1
Has abstractyes

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