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Record W2317667821 · doi:10.3327/jaesj.42.604

Issues and Suggestions for Public Perception of the Safety of the High-Level Radioactive Waste Disposal. Through Analyzing the Case of the Environmental Assessment and Review Process for the Nuclear Fuel Waste Management and Disposal Concept of Canada.

2000· article· en· W2317667821 on OpenAlexaboutno aff
Shuichi SAKAMOTO, Keiji Kanda

Bibliographic record

VenueJournal of the Atomic Energy Society of Japan / Atomic Energy Society of Japan · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
Fundersnot available
KeywordsRadioactive wasteNuclear fuel cycleLegislationSpent nuclear fuelProcess (computing)High-level wasteWaste managementWaste disposalEnvironmental impact assessmentGovernment (linguistics)Public participationBusinessEngineeringEnvironmental planningEnvironmental sciencePublic administrationComputer sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

The concerns of the Japanese public about the disposal of high-level radioactive waste (HLW) increase as the nuclear fuel cycle program makes progress. For responding to public concerns, the Japanese government is taking measures of developing the framework of the HLW disposal project such as preparing legislation and establishing the implementing entity. The activities for public acceptance of this project have been initiated recently. In the process of siting, the implementing entity will be required to gain public confidence in the safety of the disposal concept. This paper first summarizes the technical aspects of the HLW disposal projects in various countries. Then, it discusses the issues for public perception of the safety of the HLW disposal with analyzing the case of the environmental assessment and review process for the nuclear fuel waste management and disposal concept administered in Canada and makes suggestions for future steps to be taken in Japan.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.277
Teacher spread0.261 · 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 designQualitative
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

Citations1
Published2000
Admission routes1
Has abstractyes

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