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Record W2129132279 · doi:10.1109/pac.1989.73321

Exploring the beam parameter space of a CW RFQ proton accelerator

2003· article· en· W2129132279 on OpenAlexaff
G. E. McMichael, Gary Arbique, J. C. Brown, B. G. Chidley, R. M. Hutcheon, M.S. de Jong, J.Y. Sheikh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsRadio-frequency quadrupoleThermal emittanceBeam (structure)PhysicsDuty cycleQuadrupoleInjectorProtonLinear particle acceleratorRadio frequencyRange (aeronautics)Energy (signal processing)Nuclear physicsNuclear engineeringPower (physics)Atomic physicsOpticsElectrical engineeringAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

RFQ1, a 100% duty factor radio-frequency quadrupole (RFQ) to accelerate 75 mA of protons to a final energy of 0.6 MeV, is a testbed for a wide range of high-power RFQ experiments. First beam was accelerated in RFQ1 in July 1988, and an experimental program to investigate the beam parameter space of the machine was started. With the initial three-beamlet ion sources adjusted to deliver 5 mA to the RFQ, only 35% of the beam was captured and accelerated to design energy. Changing to a lower emittance single-beamlet source, transmission improved to almost 80% with over 6 mA CW (continuous wave) accelerated. The realization of the replaceable component design of the RFQ, CW operation of vane coupling rings and the ability of the racetrack seals to maintain vacuum and RF integrity over large temperature gradients were demonstrated. Design deficiencies in the injector that spoil the match to the RFQ have been identified, and TRANSPORT and PARMTEQ were used to examine corrective measures.>

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.233
Teacher spread0.172 · 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 designSimulation or modeling
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
Published2003
Admission routes1
Has abstractyes

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