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Record W2033980490 · doi:10.1155/tsm.34.75

Simulation of Texture Formation in a ZrO<sub>2</sub> Film Grown on Zr–2.5%Nb

2000· article· en· W2033980490 on OpenAlexaff
H. Li, M.G. Glavicic, Jerzy A. Szpunar

Bibliographic record

VenueTexture Stress and Microstructure · 2000
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsMcGill University
Fundersnot available
KeywordsTexture (cosmology)Materials scienceCrystallographyChemical engineeringMetallurgyComputer scienceChemistryArtificial intelligenceImage (mathematics)Engineering

Abstract

fetched live from OpenAlex

A computer model is developed which is capable of simulating the texture and microstructure of the oxide grown on a Zr substrate. In this computer model, the effects of both substrate texture and microstructure in the formation of oxide texture and microstructure are taken into account. The substrate and oxide are represented with digitized unit cells. Each unit cell has an orientation characterized by three Euler angles. In the nucleation and re‐nucleation stage, the orientation of each oxide cell is determined by substrate orientation and microstructure. In the oxide grain growth stage, the orientation of each oxide cell is determined by minimizing the stress at the oxide/metal interface. In this paper, the model is applied to the simulation of the texture formation of the ZrO2 grown on a Zr–2.5%Nb substrate. Three substrates with completely different orientations and microstructures are used in the study. Good agreement between the simulated and the experimental results is obtained.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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 designSimulation or modeling
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

Citations1
Published2000
Admission routes1
Has abstractyes

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