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TRIUMF's Injector and Accelerator Cryomodules

2015· article· en· W2557506103 on OpenAlexaff
Norman Müller, P. Harmer, James Keir, David Kishi, Philipp Kolb, Alexey Koveshnikov, C. Laforge, Devon Lang, Robert Laxdal, Yanyun Ma, A.K. Mitra, Ruslan Nagimov, Ryan Smith, Bhalwinder Waraich, 刘洋 Liu Yang, Zhongyuan Yao, Vladimir Zvyagintsev

Bibliographic record

VenueJACOW · 2015
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsTRIUMF
Fundersnot available
KeywordsLinear particle acceleratorInjectorParticle acceleratorComputer scienceNuclear physicsNuclear engineeringPhysicsEngineeringBeam (structure)Mechanical engineeringOptics

Abstract

fetched live from OpenAlex

TRIUMF's ARIEL project includes a 50 MeV-10mA electron linear accelerator (e-Linac) using 1.3 GHz superconducting technology. The accelerator consists of three cryomodules; an injector cryomodule with one cavity and two accelerating cryomodules with two cavities each. One injector and one accelerator have been assembled and commissioned at TRIUMF with a second injector cryomodule being assembled for VECC in Kolkata. Both Injector and Accelerator cryomodules utilize a top-loaded cold mass design contained in a box-type cryomodule; design and early test results of both cryomodules are presented.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.596
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

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

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 head0.025
GPT teacher head0.223
Teacher spread0.198 · 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 teacher head, 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
Published2015
Admission routes1
Has abstractyes

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