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MONITORING METHODS AND DOSE ASSESSMENT FOR INTERNAL EXPOSURES INVOLVING MIXED FISSION AND ACTIVATION PRODUCTS CONTAINING ACTINIDES

2001· review· en· W2047373158 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 · 2001
Typereview
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsOntario Power Generation
Fundersnot available
KeywordsRadionuclideFission productsFissionRadiochemistryTransuranium elementActinideInterimIn vivoChemistryEnvironmental scienceMedical physicsPlutoniumMedicineNuclear chemistryNuclear physicsNeutronPhysics

Abstract

fetched live from OpenAlex

Internal dose assessment for intakes of radionuclide mixtures is a difficult task. When the radionuclide mixture contains both the easy to detect gamma emitters, e.g., 60Co and 95Zr, and difficult to detect alpha emitters such as 239Pu and 241Am, a single monitoring method, such as in-vivo counting, is inadequate for detection and dose assessment. Recent experience with task related monitoring for such radionuclide mixtures at Ontario Power Generation CANDU nuclear power plants has offered an opportunity to review this topic and suggest a strategy for monitoring that involves a combination of in-vivo and in-vitro methods. Using the radionuclide composition data in a mixture from an actual case as an example, this paper describes a monitoring strategy for mixed fission and activation products, including the advantages and pitfalls of reliance on surrogate radionuclides for signaling the presence of actinides in the mixture. The described monitoring strategy is consistent with the recommendations of ICRP Publication 78, which advocates a "combination of techniques so as to make the best possible evaluation of an unusual situation, for example, a programme of both body activity and excreta measurements." The use of experience and professional judgement for interpreting the combined in-vivo and in-vitro data for interim and ultimate intake and dose assessment is discussed and emphasized.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.186
GPT teacher head0.496
Teacher spread0.310 · 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