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Record W2123312853 · doi:10.1139/p01-126

Deuterides of light elements: low-temperature thermonuclear burn-up and applications to thermonuclear fusion problems

2002· article· en· W2123312853 on OpenAlexafffundvenue
Alexei M. Frolov, Vedene H. Smith, Gary T. Smith

Bibliographic record

VenueCanadian Journal of Physics · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermonuclear fusionPhysicsNuclear physicsAtomic physicsPlasma

Abstract

fetched live from OpenAlex

Thermonuclear burn-up and thermonuclear applications are discussed for a number of deuterides and DT hydrides of light elements. These deuterides and corresponding DT hydrides are often used as thermonuclear fuels or components of such fuels. In fact, only for these substances thermonuclear energy gain exceeds (at some densities and temperatures) the bremsstrahlung loss and other high-temperature losses, i.e., thermonuclear burn-up is possible. Herein, thermonuclear burn-up in these deuterides and DT hydrides is considered in detail. In particular, a simple method is proposed to determine the critical values of the burn-up parameter xc for these substances and their mixtures at different temperatures and densities. The results for equimolar DT mixtures coincide quite well with the results of previous calculations. Also, the natural or Z limit is determined for low-temperature thermonuclear burn-up in the deuterides of light elements. PACS Nos.: 28.52Cx, 47.40Nm

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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

Citations10
Published2002
Admission routes3
Has abstractyes

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