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Cryogenics Infrastructure at TRIUMF's Particle Accelerator Facilities

2018· article· en· W2932740544 on OpenAlex
Alexey Koveshnikov, Yu. Bylinskii, Geoff Hodgson, David Kishi, Robert Laxdal, Ruslan Nagimov, Dimo Yosifov

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJACOW · 2018
Typearticle
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsTRIUMF
Fundersnot available
KeywordsParticle acceleratorNuclear physicsNuclear engineeringCryogenicsSuperconducting Super ColliderPhysicsEnvironmental scienceEngineeringBeam (structure)

Abstract

fetched live from OpenAlex

Cryogenic infrastructure is an indispensable part of TRIUMF accelerator facilities. At the moment TRIUMF operates three helium cryogenic systems supporting operation of three major accelerator systems: 520 MeV proton cyclotron, superconductive radio-frequency (SRF) heavy ion linear accelerator at the Rare Isotope Beams (RIB) facility, and SRF electron linear accelerator (e-linac) at Advanced Rare IsotopE Laboratory (ARIEL). Applications of cryogenic thermal loads vary from cryogenic absorption pumping of the cyclotron vacuum tank to cryogenic cooling of superconducting (SC) RF cavities of production accelerators and support of research and development at SRF department. Wide range of production techniques for cryogenic refrigeration includes helium refrigerators based on both piston and turbine expansion coldboxes for both 4 K and 2 K temperature cryogenic loads. This paper presents the details of TRIUMF cryogenic systems as well as operational experience of various cryogenic installations.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.999

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)0.0020.000

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 head0.016
GPT teacher head0.220
Teacher spread0.204 · 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