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Study of the Influence of Radionuclide Biokinetics on the Efficiency of In Vivo Counting Using Monte Carlo Simulation

2009· article· en· W2015283016 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 · 2009
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsHealth Canada
Fundersnot available
KeywordsCounting efficiencyMonte Carlo methodIn vivoRadionuclideInternal dosimetryWhole body countingChemistryRadiochemistryEnvironmental scienceBiological systemNuclear medicineComputer scienceMathematicsStatisticsDosimetryPhysicsNuclear physicsMedicineDetector

Abstract

fetched live from OpenAlex

To improve calibration methods of in vivo counting, our laboratory has developed a computer tool to model internal contamination and assess in vivo activity and corresponding organ absorbed doses. The aim of the recent work was to define a more realistic source based on biokinetic models. The influence of the biokinetic parameters on the in vivo counting was studied through the simulation of an acute inhalation intake of (241)Am. The tissue distribution of activity predicted by the biokinetic model was visualized. Two equivalent methods for determination of the efficiency related to the total activity distributed in the body were used. The comparison between the efficiency taking the biokinetics into account and the classically estimated efficiency quantifies the influence of the activity distribution in the body and provides conversion factors for correcting the classical efficiency to account for biokinetics.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.158
GPT teacher head0.447
Teacher spread0.289 · 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