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Record W1981702672 · doi:10.1093/rpd/ncn100

Can the HML's sliced BOMAB phantom be used in any whole body counter with a reduced number of sources?

2008· article· en· W1981702672 on OpenAlexaff
Gary H. Kramer, B. M. Hauck, Shuai Dang, Kevin Capello

Bibliographic record

VenueRadiation Protection Dosimetry · 2008
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsHealth Canada
Fundersnot available
KeywordsImaging phantomRange (aeronautics)PlanarPhysicsEnergy (signal processing)Computer scienceMaterials scienceOpticsComputer graphics (images)

Abstract

fetched live from OpenAlex

The sliced Bottle Manikin Absorber (BOMAB) phantom was originally proposed as an alternative to a commercially available phantom, but it suffers from the disadvantage of containing over 160 sources that need to be manufactured; however, it was found that the number of slices could be reduced substantially and that two slices in the sliced phantom gave the same performance characteristics over a wide energy range as a conventional BOMAB phantom for a particular counting system. This work explores the adaptability of this phantom to another counting geometry. The response of the Human Monitoring Laboratory's whole-body counter measuring this phantom with a decreasing number of planar sources has been modelled using MCNP5 over a wide energy range (122-2754 keV). It was found that the best agreement was obtained when the phantom contained 10 sources, 1 in the mid point of each section. As this is a different result from a previous finding, any other counting geometry will have to be assessed to determine the optimum loading if the sliced phantom is to be used. Also, it is clear that this type of phantom cannot be used for an intercomparison that will encounter different counting geometries, unless it contains a full loading of sources.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.269
Teacher spread0.251 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2008
Admission routes1
Has abstractyes

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