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A NEW PRODUCTION PROCESS FOR UCx TARGETS FOR RADIOACTIVE ISOTOPE BEAMS AT TRIUMF

2018· article· en· W2990806806 on OpenAlexaff
Marla Cervantes, Pauline Fouquet-Métivier, A. Gottberg, P. Kunz, Laura Lambert, Anders Mjøs, John Wong

Bibliographic record

VenueJACOW · 2018
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsTRIUMF
Fundersnot available
KeywordsProduction (economics)Process (computing)IsotopeNuclear physicsRadiochemistryPhysicsComputer scienceNuclear engineeringChemistryEngineeringProgramming language

Abstract

fetched live from OpenAlex

TRIUMF has the objective of producing radioactive isotope beams (RIB) using the ISOL method. Radioactive isotopes are used in experiments in different areas of science. At the TRIUMF-ISAC facility, a 500 MeV proton driver beam impinges onto different targets and induces nuclear reactions in them. The isotopes obtained in this way then diffuse out of the target material before they are ionized and extracted to form an isotope beam. Targets of uranium carbide with excess of graphite (UCx) are the most requested targets at TRIUMF. ARIEL, TRIUMF's flagship project, aims at increasing the radioactive isotope production capabilities to satisfy the growing demand of radioactive isotopes. The current production method of UCx targest does not have the means to supply enough UCx targets to satisfy ARIEL's demand, therefore, a new method for efficient UCx target material synthesis is being developed.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.260
Teacher spread0.244 · 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
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

Citations2
Published2018
Admission routes1
Has abstractyes

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