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Preliminary commissioning of a prototype solid tank optical CT scanner for 3D radiation dosimetry

2023· article· en· W4388706198 on OpenAlexaff
A Ogilvy, S.M. Collins, Maria Guenter, David Huang, Warren Hare, Michelle Hilts, Andrew Jirasek

Bibliographic record

VenueJournal of Physics Conference Series · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsKelowna General HospitalOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsScannerImaging phantomImage resolutionOpticsDosimeterComputer scienceDosimetryNuclear medicinePhysicsRadiationMedicine

Abstract

fetched live from OpenAlex

Abstract Optical computed tomography (CT) is one of the leading modalities for imaging gel dosimeters. In previous research, it was shown that a design could significantly reduce the volume of the refractive index baths that are commonly found in optical CT systems. The proposed scanner has been manufactured and is in process of being commissioned. The rays refract through the system and their paths are estimated using a ray-tracing simulator. Reconstruction is preformed through algebraic reconstruction technique and iterated using the FISTA-TV algorithm. A Sylgard®184 edge phantom was created to commission the scanner’s spatial resolution. The scanner is capable of a 0.929 mm−1 spatial resolution, although the spatial resolution is highly dependent on the number of iterations, and method of processing the edge spread function.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.317
Teacher spread0.297 · 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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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