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Record W2084340976 · doi:10.1155/2014/178360

Thermal Conductivity of Uranium Nitride and Carbide

2014· article· en· W2084340976 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Nuclear Energy · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsUniversity of Saskatchewan
FundersWestern Canada Research Grid
KeywordsCondensed matter physicsThermal conductivityUraniumElectronMaterials scienceElectronic structureNitrideAntiferromagnetismMagnetic momentFerromagnetismPhysicsNuclear physicsMetallurgyNanotechnology

Abstract

fetched live from OpenAlex

We investigate the electronic thermal conductivity of alternative fuels like uranium nitride and uranium carbide. We evaluate the electronic contribution to the thermal conductivity, by combining first-principles quantum-mechanical calculations with semiclassical correlations. The electronic structure of UN and UC was calculated using Quantum Espresso code. The spin polarized calculations were performed for a ferromagnetic and antiferromagnetic ordering of magnetic moments on uranium lattice and magnetic moment in UC was lower than in UN due to stronger hybridization between 2p electrons of carbon and 5f electrons of uranium. The nonmagnetic electronic structure calculations were used as an input to BolzTrap code that was used to evaluate the electronic thermal conductivity. It is predicted that the thermal conductivity should increase with the temperature increase, but to get a quantitative agreement with the experiment at higher temperatures the interaction of electrons with phonons (and electron-electron scattering) needs to be included.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.615

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)0.0010.000

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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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