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Record W2059246562 · doi:10.1177/1946756714536142

Lunar Helium-3 Fuel for Nuclear Fusion

2014· article· en· W2059246562 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueWorld Futures Review · 2014
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsContext (archaeology)ElectricityTonRadioactive wasteNuclear powerEnvironmental scienceSpent nuclear fuelNatural resource economicsWaste managementEngineeringGeologyNuclear physicsPhysicsGeography

Abstract

fetched live from OpenAlex

Nuclear fusion of helium-3 ( 3 He) can be used to generate electrical power with little or no radioactive waste and no carbon emissions. Some forty-four tons of this fuel could meet the electricity needs of the United States for a year. Although rare on Earth, an estimated one million tons of 3 He has collected on the surface of the moon. While it would cost approximately US$17 billion to develop a mine producing one ton of 3 He per year, such an operation is commercially viable over the medium term given the estimated value of that ton of fuel: US$3.7 billion. This article outlines the technical and economic issues related to 3 He and its extraction, and it presents a novel approach to estimating the worth of the fuel. The potential of 3 He as a future energy source is set in the context of global energy forecasts and international efforts to investigate lunar 3 He resources—including a recent Chinese mission.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.510
Threshold uncertainty score0.507

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 communication0.0000.000
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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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