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Record W321165875 · doi:10.13182/nt01-a3192

Economic Analysis on Direct Use of Spent Pressurized Water Reactor Fuel in CANDU Reactors—III: Spent DUPIC Fuel Disposal Cost

2001· article· en· W321165875 on OpenAlexaboutno aff
Won Il Ko, Hangbok Choi, Gyuhong Roh, Myung Seung Yang

Bibliographic record

VenueNuclear Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
FundersKorea Advanced Institute of Science and TechnologyKorea Atomic Energy Research InstituteKorea Institute of Science and Technology
KeywordsSpent nuclear fuelEnvironmental sciencePressurized water reactorWaste managementUraniumNatural uraniumSpent fuel poolElectricity generationDepleted uraniumWaste disposalCost estimateNuclear engineeringEngineeringMaterials science

Abstract

fetched live from OpenAlex

The disposal costs of spent pressurized water reactor (PWR), Canada deuterium uranium (CANDU) reactor, and DUPIC fuels have been estimated based on available literature data and the engineering design of a spent CANDU fuel disposal facility by the Atomic Energy of Canada Limited. The cost estimation was carried out by the normalization concept of total electricity generation. Therefore, the future electricity generation scale was analyzed to evaluate the appropriate capacity of the high-level waste disposal facility in Korea, which is a key parameter of the disposal cost estimation. Based on the total electricity generation scale, it is concluded that the disposal unit costs for spent CANDU natural uranium, CANDU-DUPIC, and PWR fuels are 192.3, 388.5, and 696.5 $/kg heavy element, respectively.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.200
Teacher spread0.190 · 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 designSimulation or modeling
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

Citations14
Published2001
Admission routes1
Has abstractyes

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