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Commissioning of the ARIEL E-LINAC Beam Loss Monitor System

2019· article· en· W3205009034 on OpenAlexaff
M. Alcorta, Alex d'Angelo, Don Dale, H. Hui, B.M. Humphries, Shane Koscielniak, Kevin Langton, A. Lennarz, R. Nussbaumer, Thomas Planche, Mike Rowe, Stephanie Rädel

Bibliographic record

VenueJACOW · 2019
Typearticle
Languageen
FieldEngineering
TopicParticle Accelerators and Free-Electron Lasers
Canadian institutionsTRIUMF
Fundersnot available
KeywordsLinear particle acceleratorBeam (structure)Project commissioningNuclear engineeringMedical physicsPhysicsEngineeringOpticsPolitical science

Abstract

fetched live from OpenAlex

The commissioning of the Advanced Rare Isotope Electron Linac (ARIEL) facility at TRIUMF is underway. The 30 MeV e-linac has successfully been commissioned to 100 W, and to further increase the power to 1 kW the beam loss monitor system (BLM) for fast Machine Protection must be fully operational. There are currently two types of BLMs employed in the e-linac; long-ionization chambers (LIC) and scintillators, consisting of a small BGO coupled to a PMT. A front-end beam loss monitor board was designed at TRIUMF to meet the strict requirements of the BLMs: a trip of the beam occurs on 100 nC in 100 ms of integrated beam loss, and the trip must occur in 10 us. This contribution will report on the status of the 1 kW BLM system commissioning and will give an outlook as the power is increased to the full 300 kW.

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)0.005
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.006

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 head0.004
GPT teacher head0.176
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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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