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Record W2014232513 · doi:10.1063/1.1150336

Design of an electron cyclotron resonance ion source for the isotope separator and accelerator at TRIUMF

2000· article· en· W2014232513 on OpenAlexaff
D. Yuan, K. Jayamanna, M. Dombsky, D. Louie, S. Kadantsev, R. Keitel, T. Kuo, M. McDonald, M. Olivo, P. W. Schmor

Bibliographic record

VenueReview of Scientific Instruments · 2000
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsTRIUMF
Fundersnot available
KeywordsElectron cyclotron resonanceIon sourceThermal emittanceCyclotronIon beamMaterials scienceAtomic physicsParticle acceleratorIonFourier transform ion cyclotron resonanceBeam emittanceSeparator (oil production)IonizationBeam (structure)PlasmaNuclear physicsPhysicsOptics

Abstract

fetched live from OpenAlex

A 2.45 GHz electron cyclotron resonance ion source has been designed and built to ionize radioactive atoms and produce single charged ion beams for the isotope separator and accelerator at TRIUMF. The source is characterized by its short axial length (8 cm) and the small pair of magnetic coils (diam=φ26 cm) placed adjacent to the source cavity. A small diameter quartz tube is inserted in the cavity to confine the plasma to a small volume and to shorten total ion transient time for the exotic gaseous elements with short and intermediate half life. The computer code POISSON is used to design the magnetic coils. A three-electrode extraction system simulated with the code IGUNE is used to study the beam extraction efficiency. In this article the design of the source, extraction system, and the preliminary results on beam emittance and energy spread are presented; the results on ion transient time, ionization efficiency, and beam extraction efficiency are presented in a separate article.

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)0.000
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.258
Teacher spread0.240 · 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

Citations3
Published2000
Admission routes1
Has abstractyes

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