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Record W3119512132 · doi:10.15392/bjrs.v8i3a.1368

Advanced Heavy Water Reactor: A New Step Toward Sustainability

2021· article· en· W3119512132 on OpenAlexaff
Ricardo Bastos Smith, Mahima Sachdeva, Indranil Bisuri, Roberto Vicente

Bibliographic record

VenueBrazilian Journal of Radiation Sciences · 2021
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsPlutoniumNuclear transmutationThorium fuel cycleEnvironmental scienceSustainabilityNuclear engineeringUraniumMOX fuelThoriumSpent nuclear fuelNuclear powerWaste managementActinideEngineeringNuclear physicsPhysics

Abstract

fetched live from OpenAlex

One of the great advances in the current evolution of nuclear power reactors is occurring in India, with the Advanced Heavy Water Reactor (AHWR). It is a reactor that uses thorium as part of its fuel, which in its two fueling cycle options, in conjunction with plutonium or low enriched uranium, produces energy at the commercial level, generating less actinides of long half-life and inert thorium oxide, which leads to an optimization in the proportion of energy produced versus the production of burnt fuels of the order of up to 50%. The objective of this work is to present the most recent research and projects in progress in India, and how the expected results should be in compliance with the current sustainability models and programs, especially the "Green Chemistry", a program developed since the 1990s in the United States and England, which defines sustainable choices in its twelve principles and that can also be mostly related to the nuclear field. Nevertheless, in Brazil, for more than 40 years there has been the discontinuation of research for a thorium-fueled reactor, and so far there has been no prospect of future projects. The AHWR is an important example as an alternative way of producing energy in Brazil, as the country has the second largest reserve of thorium on the planet.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.471

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 communication0.0000.001
Open science0.0000.000
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 head0.017
GPT teacher head0.292
Teacher spread0.275 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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