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THE STANDFAST WHOLE BODY COUNTER: EFFICIENCY AS A FUNCTION OF BOMAB PHANTOM SIZE AND ENERGY MODELED BY MCNP5

2007· article· en· W2037104107 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealth Physics · 2007
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsHealth Canada
Fundersnot available
KeywordsImaging phantomEnclosureMonte Carlo methodCalibrationPhysicsPosition (finance)Photon countingPhotonCounting efficiencyEnergy (signal processing)Whole body countingOpticsComputational physicsMathematicsDetectorComputer scienceNuclear physicsStatistics

Abstract

fetched live from OpenAlex

The StandFast whole body counter has been modeled using Monte Carlo simulations to examine the effect of phantom size, photon energy, and position of the phantom within the counting enclosure on the counting efficiency. The first geometry, the manufacturer's recommended positioning, was found to have the higher counting efficiencies and the most dependence on phantom size. The second position, where the phantom is at the back of the counting enclosure, had lower counting efficiencies, and hence higher minimum detectable activities, by a factor of between 1.3 to 2.1 when compared with the first geometry; however, for emergency response where accuracy is to be preferred over sensitivity, this geometry would be the better choice. A unified calibration equation was also developed for the StandFast so that it is possible to predict the counting efficiency as a function of photon energy and size to within 11%.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.290
Teacher spread0.282 · 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