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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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.003

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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