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LUNG COUNTING: SUMMING TECHNIQUES TO REDUCE THE MDA

2003· article· en· W2091760227 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHealth Physics · 2003
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsCameco (Canada)Health Canada
Fundersnot available
KeywordsNuclideEnvironmental scienceUraniumRadionuclideNuclear medicineRadiochemistryMedical physicsPhysicsChemistryNuclear physicsMedicine

Abstract

fetched live from OpenAlex

The new dose limits recently adopted in Canada (and elsewhere in the world) have made it more difficult to detect some radionuclides by in vivo counting at the average dose limit of 20 mSv. This is particularly true for natural uranium. Two techniques have been developed by the Human Monitoring Laboratory to reduce the Minimum Detectable Activity (MDA) for the lung counting of this nuclide. The first technique, developed in collaboration with Cameco, is to either sum sequential counts of an individual or to sum spectra of a group of workers similarly occupationally exposed. This technique offers a reduction in the MDA of up to a factor of three. The second technique, developed in collaboration with CNEN, involves the summing of photopeaks within an individual spectrum and offers a reduction in the MDA of up to a factor of two.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.143
GPT teacher head0.470
Teacher spread0.327 · 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