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Opportunities for improving the performance of LCV micelle gel dosimeters: II. Recipe optimization

2015· article· en· W2073031055 on OpenAlexaff
A T Nasr, K M Alexander, T Olding, L J Schreiner, Kimberley B. McAuley

Bibliographic record

VenueJournal of Physics Conference Series · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsCancer Care South EastQueen's University
Fundersnot available
KeywordsRecipeMicelleGelatinAmmoniumChemistryChromatographyAmmonium bromideNuclear chemistryMaterials sciencePulmonary surfactantOrganic chemistryAqueous solutionBiochemistryFood science

Abstract

fetched live from OpenAlex

Designed experiments and empirical models are used to optimize a Leuco Crystal Violet (LCV) micelle gel recipe to improve dose sensitivity and initial colour. The optimized recipe contains 0.75 mM LCV, 17.0 mM Cetyl Trimethyl Ammonium Bromide (CTAB), 120 mM 2,2,2-trichloroethanol (TCE), 25.0 mM tri-chloro acetic acid (TCAA), 4 wt% gelatin and ~96 wt% water. Dose sensitivity of the optimized gel is 1.5 times higher than Jordan's standard LCV gel. Spatial integrity of the 3D dose distribution information in 1L jar phantoms made using this recipe is maintained for more than two weeks.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.059
GPT teacher head0.273
Teacher spread0.214 · 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".

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Citations1
Published2015
Admission routes1
Has abstractyes

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