Advanced Heavy Water Reactor: A New Step Toward Sustainability
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".