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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

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 3 He, 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 3 He 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 m 3 (STP) per annum, varying with the tritium activity buildup in the moderator. The harvesting of 3 He would generate approximately 12.7 m 3 (STP) of 3 He, worth more than $30M at current market rates, over a typical 25-year operating cycle of the PHWR. This paper discusses the production of 3 He in the moderator of a PHWR and its extraction from the 4 He 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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0030.003

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.

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