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Record W4386954236 · doi:10.1038/s41598-023-41687-0

Estimating 225Ac yield in thorium metal targets

2023· article· en· W4386954236 on OpenAlexafffund
Mona Rahmani, D. Mark Martinez

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaTRIUMF
KeywordsThoriumYield (engineering)BiologyMedicinePhysicsMaterials scienceMetallurgyNuclear physics

Abstract

fetched live from OpenAlex

In this work we estimate the yield of the radioisotope [Formula: see text] in a thorium metal target geometry similar to that described by Roberston et al.. We do so in three different yet complimentary studies. In the first study, we pose a three-way coupled time-dependent model describing beam position, temperature field, and local growth of the activity of the radioisotope and solve this numerically. In the second study, we present an analytical solution of the model equations for a generalized solid target in the "beam-thin" limit, i.e. where only a small fraction of the incoming energy of the proton beam is deposited into the thorium material. In the third study, we use the insight gained from the analytical solution and describe an operating strategy to maximize yield by modulating the beam flux temporally.

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.305
Teacher spread0.278 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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