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Record W3008621498 · doi:10.1051/epjconf/202022906003

Medical isotope collection from ISAC targets

2020· article· en· W3008621498 on OpenAlexaff
P. Kunz, C. Andreoiu, Victoria Brown, Marla Cervantes, J. Even, F. H. Garcia, A. Gottberg, J. Lassen, Valery Radchenko, Caterina F. Ramogida, Andrew K. H. Robertson, Paul Schaffer, Rozhannaa Sothilingam

Bibliographic record

VenueEPJ Web of Conferences · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsUniversity of British ColumbiaSimon Fraser UniversityTRIUMF
Fundersnot available
KeywordsIsotopeNuclear astrophysicsNuclear physicsIsotope separationCyclotronPhysicsRecoilRadiochemistryProtonBeamlineBeam (structure)ChemistryOptics

Abstract

fetched live from OpenAlex

The ISAC facility (Isotope Separation and Acceleration) at TRIUMF has recently started to provide isotopes for pre-clinical nuclear medicine studies. By irradiating ISOL (Isotope Separation OnLine) targets with a 480 MeV proton beam from the TRIUMF H- cyclotron, the facility can deliver a large variety of radioactive isotope beams (RIB) for research in the fields of nuclear astrophysics, nuclear structure and material science with half-lives down to a few milliseconds via an electrostatic beamline network. For the collection of medical isotopes, typically with half-lives in the range of hours or days, we have developed a compact apparatus for the implantation of mass-separated RIB on a target disc at energies between 20-55 keV. In this paper, we also discuss two different retrieval methods of the implanted activity from the implantation target: by chemical etching of the target surface and by recoil collection of implanted alpha emitters.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.248
Teacher spread0.232 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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