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Record W3124893329 · doi:10.1080/00295450.2020.1812318

Reactor Physics Analysis Assessment of Feasibility of Using Advanced, Nonconventional Fuels in a Pressure Tube Heavy Water Reactor to Destroy Long-Lived Fission Products

2021· article· en· W3124893329 on OpenAlexaff
Blair P. Bromley, Ashlea V. Colton

Bibliographic record

VenueNuclear Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsBlanketNuclear engineeringFission productsThorium fuel cycleFissionHeavy waterPressurized water reactorEnvironmental scienceNatural uraniumNuclear reactor coreProduction rateNuclear physicsUraniumMaterials sciencePhysicsNeutronProcess engineeringEngineeringThorium

Abstract

fetched live from OpenAlex

Lattice physics and core physics studies have been carried out to investigate the feasibility of destroying long-lived fission products (LLFPs) using special target fuel bundles in blanket fuel channels in a seed-blanket core in a pressure tube heavy water reactor (PT-HWR) fueled primarily with natural uranium. Results indicate that it should be feasible to achieve net zero production of LLFPs such as 79Se and 129I using one to two dedicated blanket channels containing LLFP target bundles. With 60 blanket channels, the net production rate of 99Tc or 126Sn could be reduced by 75% or more. Further design modifications may be able to achieve net zero production for most LLFPs, with the exception of 135Cs, which would require isotopic separation.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.021
GPT teacher head0.273
Teacher spread0.252 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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