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Record W2128502847 · doi:10.1088/0967-3334/34/5/503

Design of a phantom equivalent to measure bone-fluorine in a human's hand via delayed neutron activation analysis

2013· article· en· W2128502847 on OpenAlexaff
Farshad Mostafaei, Fiona E. McNeill, David R. Chettle, W. V. Prestwich, Michael Inskip

Bibliographic record

VenuePhysiological Measurement · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsImaging phantomNeutron activation analysisCalibrationFluorineFluorideSIGNAL (programming language)ContaminationMaterials scienceBiomedical engineeringRadiochemistryOpticsChemistryComputer scienceEngineeringPhysicsMetallurgy

Abstract

fetched live from OpenAlex

Fluorine is an element that can be either beneficial or harmful, depending on the total amount accumulated in the teeth or bones. In our laboratory, we have developed a non-invasive technique for the in vivo measurement of fluoride in bone using neutron activation analysis and performed the first pilot human study. Fluoride in humans is quantified by comparing the γ-ray signal from a person to the γ-ray signal obtained from appropriate anthropomorphic calibration phantoms. An identified problem with existing fluoride phantoms is contamination with aluminum. Aluminum creates an interfering γ-ray signal which, although it can be subtracted out, increases the uncertainty in the measurement and worsens the detection limit. This paper outlines a series of studies undertaken to develop a better calibration phantom for fluorine measurement, which does not have aluminum contamination.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.078
GPT teacher head0.285
Teacher spread0.207 · 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".

Quick stats

Citations12
Published2013
Admission routes1
Has abstractyes

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