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Quantification of low dose signal in EPR tooth dosimetry - a novel approach

2003· article· en· W2317807914 on OpenAlex
Rao Khan, Doug Boreham, W.J. Rink

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

VenueRadiation Protection Dosimetry · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRadiation Effects and Dosimetry
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDosimetrySIGNAL (programming language)SpectrometerTooth enamelElectron paramagnetic resonanceMaterials scienceExtrapolationDose profileRadiationDosimeterAbsorbed doseNuclear medicineNuclear magnetic resonanceOpticsEnamel paintPhysicsMedicineMathematicsComputer scienceStatistics

Abstract

fetched live from OpenAlex

For radiation exposures below 100 mGy, the dosimetric signal in tooth enamel is too small to be measured by using the traditional dose reconstruction procedure. This is because low amplitude zero-added-dose signal can not be identified in an EPR spectrometer. A technique is presented wherein, zero-added-dose signal. when amplified by a proper known dose, can be measured in the EPR spectrometer. Mathematically, the accidental dose x is modified by a known amount of exposure, y (large enough so that the signal is now visible), and total exposure becomes x' = x + y, which is the modified-zero-added dose. The exposure x' is then quantified using the conventional backward extrapolation method and the accidental dose can be measured. In a laboratory controlled experiment, the feasibility of dose reconstruction in the 100 mGy range has been demonstrated. This may enable measurements of dose even due to suspected low exposure in tooth enamel.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.022
GPT teacher head0.225
Teacher spread0.204 · 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