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Record W4367284478 · doi:10.1021/cen-10001-buscon1

<sup>3</sup>He availability to increase with new supply deal

2022· article· en· W4367284478 on OpenAlex
Craig Bettenhausen

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueC&EN Global Enterprise · 2022
Typearticle
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCryogenicsNuclear powerLiquid heliumProduct (mathematics)HeliumNuclear engineeringPhysicsNuclear physicsEngineeringAtomic physicsMathematics

Abstract

fetched live from OpenAlex

The industrial gas giant Air Liquide has signed a long-term agreement to purchase helium-3 from a Canadian nuclear power firm. The arrangement creates the first significant private supply of the ultrarare gas, a light isotope of helium used in deep cryogenics, nuclear science, and quantum computing. Air Liquide will take 3 He that Laurentis Energy Partners extracts from a nuclear power by-product, further purify it, and package it for sale. Jennifer Chapin, director of projects at Laurentis, says the initial output will be between 5,000 and 10,000 L per year. “This really does present a shift in terms of market availability,” she says. Though the gas can be used for medical imaging and other nuclear science, Patrick Wikus, a cryogenics expert at the scientific instrument maker Bruker, expects most of the new supply to be employed in quantum computing, which requires operating temperatures near 0 K. Conventional liquid 4 He

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.999

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)0.0020.000

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 head0.008
GPT teacher head0.217
Teacher spread0.210 · 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