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Record W2319500139 · doi:10.12943/anr.2013.00010

Direct Harvesting of Helium-3 (<sup>3</sup>He) From Heavy Water Nuclear Reactors

2013· article· en· W2319500139 on OpenAlexaffvenue
G. Bentoumi, R. Didsbury, G. Jonkmans, Luı́s Rodrigo, B. Sur

Bibliographic record

VenueAECL Nuclear Review · 2013
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsHeavy waterTritiumNuclear engineeringEnvironmental scienceNuclear physicsHeliumNeutron moderatorDeuteriumRadiochemistryNeutronChemistryWaste managementNeutron temperaturePhysicsNeutron cross section

Abstract

fetched live from OpenAlex

The thermal neutron activation of deuterium inside a heavy-water-moderated or -cooled nuclear reactor produces a build-up of tritium in the heavy water. The in situ decay of tritium can, for certain reactor types and operating conditions, produce potentially useable amounts of 3He, which can be directly extracted via the heavy-water cover gas without first separating, collecting and storing tritium outside the reactor. It is estimated that the amount of 3He available for recovery from the moderator cover gas of a 700 MWe class Pressurized Heavy Water Reactor (PHWR) ranges from 0.1 to 0.7 m3 (STP) per annum, varying with the tritium activity buildup in the moderator. The harvesting of 3He would generate approximately 12.7 m3 (STP) of 3He, worth more than $30M at current market rates, over a typical 25-year operating cycle of the PHWR. This paper discusses the production of 3He in the moderator of a PHWR and its extraction from the 4He moderator cover gas system using conventional methods.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.184
Teacher spread0.175 · 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 designBench or experimental
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

Citations5
Published2013
Admission routes2
Has abstractyes

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