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Record W1949590126 · doi:10.1007/978-3-319-12090-4_15

Radioactive Waste Management After Fukushima Daiichi Accident

2014· book-chapter· en· W1949590126 on OpenAlexaff
Shinya Nagasaki

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRadioactive wasteWaste managementNuclear powerEnvironmental scienceRadionuclideRadioactive contaminationSpent nuclear fuelHuman decontaminationNuclear power plantEngineering

Abstract

fetched live from OpenAlex

The categories of radioactive wastes have markedly changed due to the Fukushima Daiichi Nuclear Power Station accident. In addition to the conventional radioactive wastes such as high-level radioactive waste, the designated wastes, the wastes generated by decontamination work such as contaminated soil, the wastes contaminated with radionuclides or nuclear fuel which were generated within the on-site area of the nuclear power station, the spent nuclear fuel and debris, and the contaminated water are now critically required to be taken into account. The technological and legal schemes, by which these radioactive wastes will be appropriately processed and disposed of, must be established as soon as possible. These schemes also have to be widely supported by the public and society. This chapter gives an overview of the concept of radioactive waste management and discusses the problems and challenges to be solved for the management of all types of radioactive wastes and for the sustainable use of nuclear energy in the 21st century.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.006
GPT teacher head0.178
Teacher spread0.173 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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