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Record W2467793269 · doi:10.1115/1.2010-may-2

Too Good to Leave on the Shelf

2010· article· en· W2467793269 on OpenAlexaff
David J. LeBlanc

Bibliographic record

VenueMechanical Engineering · 2010
Typearticle
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsMolten saltNuclear engineeringContainment (computer programming)Waste managementPelletsNuclear powerMolten salt reactorEnvironmental scienceBlanketRadioactive wasteEngineeringProcess engineeringMaterials scienceComputer scienceMetallurgy

Abstract

fetched live from OpenAlex

This article explores the use of molten salt reactors (MSR) as a source of cheap and limitless energy for nuclear power industry. Molten salt reactors contain no fuel pellets. MSRs run at near-atmospheric pressure, so the thick-walled pressure vessels found in light-water reactors are unnecessary. Since there is no water or sodium in the reactor fluids, there is zero possibility of a steam explosion or hydrogen production within the containment. The article also highlights advantages of using MSRs in nuclear-powered bombers. Many of the drawbacks to the molten salt reactor approach have been worked out. MSR designs have very strong negative temperature and void coefficients, which act instantly, aiding safety and allowing automatic load following operation. The Molten Salt Reactor Experiment showed that maintenance and repair could be carried out smoothly and that reactor control was highly stable. The article concludes that molten salt or liquid fluoride reactors will also take a large effort, but every indication points to a power reactor that will excel in cost, safety, long-term waste reduction, resource utilization, and proliferation resistance.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.431
Threshold uncertainty score0.812

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0100.010
Open science0.0020.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.4310.336

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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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.

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

Citations8
Published2010
Admission routes1
Has abstractyes

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