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Record W2809974664 · doi:10.1097/hp.0000000000000862

Experiences of Population Monitoring Using Whole-Body Counters in Response to the Fukushima Nuclear Accident

2018· article· en· W2809974664 on OpenAlexaff
Osamu Kurihara, Chunsheng Li, M. A. López, Kotaro Tani, Takashi Nakano, Chie Takada, Takumaro Momose, Makoto Akashi

Bibliographic record

VenueHealth Physics · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsHealth Canada
Fundersnot available
KeywordsWhole body countingRadioactive contaminationPopulationCaesiumNuclear power plantEnvironmental scienceFukushima Nuclear AccidentNuclear powerNuclear engineeringIsotopeNuclear medicineRadiochemistryRadionuclidePhysicsEnvironmental healthMedicineEngineeringNuclear physicsChemistry

Abstract

fetched live from OpenAlex

Whole-body counter measurements of residents of Fukushima Prefecture have been extensively performed after the Fukushima Dai-ichi Nuclear Power Plant accident in March 2011. These measurements have demonstrated that the levels of internal contamination with radioactive cesium (Cs and Cs) in the residents are very low. This article provides an overview of and lessons learned from these whole-body counter measurements with emphasis on the technical problems encountered, and it discusses the effective use of whole-body counters for assessing the internal thyroid doses of individuals when direct measurements of I in the thyroid are difficult or impossible to implement for the total affected population in a short time after a nuclear reactor accident. The application of this dose reconstruction method requires determining the intake ratio of I to cesium isotopes at appropriate times and considers the short biological half-lives of cesium isotopes, in particular for children.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.333
Teacher spread0.302 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations27
Published2018
Admission routes1
Has abstractyes

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