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FRIB Project Status and Beam Instrumentation Challenges

2018· paratext· en· W2803881547 on OpenAlexaff
J. Wei, A. Aleksandrov, Hiroyuki Ao, Steven Beher, Nathan Bultman, F. Casagrande, Jingping Chen, S. Cogan, Chris Compton, Leo Dalesio, Kelly Davidson, K. Dixon, Nathan B. Eddy, A. Facco, F. Feyzi, V. Ganni, Andrei Ganshyn, Paul Gibson, T. Glasmacher, Leslie Hodges, Kent Holland, K. Hosoyama, Hsiao-Chaun Hseuh, Aftab M. Hussain, Masanori Ikegami, Shelly Jones, Michael Kelly, Robert Laxdal, John LeTourneau, S. Lidia, Guillaume Machicoane, F. Marti, Samuel Miller, Dan Morris, J. A. Nolen, P. N. Ostroumov, John Popielarski, Laura Popielarski, E. Pozdeyev, S. Prestemon, Thomas Russo, Kenji Saito, Stephen Stanley, Hudeki Tatsumoto, R. Webber, M. Wiseman, Ting Xu, Yoshishige Yamazaki

Bibliographic record

VenueOSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2018
Typeparatext
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsTRIUMF
FundersStrongU.S. Department of EnergyOffice of ScienceNational Science Foundation
KeywordsInstrumentation (computer programming)Computer scienceSystems engineeringEngineeringProgramming language

Abstract

fetched live from OpenAlex

With an average beam power two orders of magnitude higher than operating heavy-ion facilities, the Facility for Rare Isotope Beams (FRIB) stands at the power frontier of the accelerator family. This report summarizes the status of design, technology development, construction, commissioning, as well as path to operations and upgrades. We also highlight beam instrumentation challenges including machine protection of high-power heavy-ion beams and complications of multi-charge-state and multi-ion-species accelerations.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.942

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 communication0.0000.001
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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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 designNot applicable
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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

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