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Record W4387541827 · doi:10.1016/j.nme.2023.101535

SiC as a core-edge integrated wall solution in DIII-D

2023· article· en· W4387541827 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.

Bibliographic record

VenueNuclear Materials and Energy · 2023
Typearticle
Languageen
FieldMaterials Science
TopicFusion materials and technologies
Canadian institutionsUniversity of Toronto
FundersFusion Energy SciencesOffice of ScienceU.S. Department of Energy
KeywordsMaterials scienceSilicon carbideDIII-DGraphiteNuclear engineeringNeutronCore (optical fiber)Resilience (materials science)IrradiationCreepSiliconEnhanced Data Rates for GSM EvolutionAtomic physicsNuclear physicsComposite materialPlasmaTokamakMetallurgyPhysics

Abstract

fetched live from OpenAlex

Silicon carbide (SiC) is a promising material for use in a fusion reactor due to its low hydrogenic diffusivity, high temperature strength and resilience under neutron irradiation [1], [2]. To assess SiC as a main wall material in DIII-D, simulations with TRIM.SP and DIVIMP are performed on a well-diagnosed L-mode discharge. The effective charge, Zeff, across the separatrix is used as a figure of merit in comparing SiC to the current graphite walls. It is found that SiC is expected to reduce Zeff, potentially by as much as ∼50%. It is discussed how SiC may be expected to “self-condition” and create wall conditions similar to siliconization, further lowering Zeff due to efficient oxygen gettering. The potential benefits are reviewed and a path towards SiC walls in DIII-D is presented.

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 categoriesInsufficient payload (model declined to judge)
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 score0.167
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0030.001

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 head0.021
GPT teacher head0.232
Teacher spread0.211 · 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