MétaCan
Menu
Back to cohort
Record W2091548065 · doi:10.2172/788560

Calculation of Doses Due to Accidentally Released Plutonium From An LMFBR

2001· report· en· W2091548065 on OpenAlexaff
B.R. Fish

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsPlutoniumNuclear engineeringEnvironmental scienceBreeder reactorActinideBreeder (animal)Containment (computer programming)RadiochemistryChemistryMaterials scienceEngineeringNuclear chemistryComputer science

Abstract

fetched live from OpenAlex

Experimental data and analytical models that should be considered in assessing the transport properties of plutonium aerosols following a hypothetical reactor accident have been examined. Behaviors of released airborne materials within the reactor containment systems, as well as in the atmosphere near the reactor site boundaries, have been semiquantitatively predicted from experimental data and analytical models. The fundamental chemistry of plutonium as it may be applied in biological systems has been used to prepare models related to the intake and metabolism of plutonium dioxide, the fuel material of interest. Attempts have been made to calculate the possible doses from plutonium aerosols for a typical analyzed release in order to evaluate the magnitude of the internal exposure hazards that might exist in the vicinity of the reactor after a hypothetical LMFBR (Liquid-Metal Fast Breeder Reactor) accident. Intake of plutonium (using data for {sup 239}Pu as an example) and its distribution in the body were treated parametrically without regard to the details of transport pathways in the environment. To the extent possible, dose-response data and models have been reviewed, and an assessment of their adequacy has been made so that recommended or preferred practices could be developed.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0170.000

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 head0.296
Teacher spread0.274 · 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 teacher head, not a consensus.

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

Citations4
Published2001
Admission routes1
Has abstractyes

Explore more

Same topicRadioactive contamination and transferFrench-language works237,207