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Record W2128133115 · doi:10.1017/s1431927614001664

Microstructure Refinement of Cold-Sprayed Copper Investigated By Electron Channeling Contrast Imaging

2014· article· en· W2128133115 on OpenAlexaff
Yinyin Zhang, Nicolas Brodusch, Sylvie Descartes, Richard R. Chromik, Raynald Gauvin

Bibliographic record

VenueMicroscopy and Microanalysis · 2014
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsMcGill University
Fundersnot available
KeywordsEquiaxed crystalsMicrostructureMaterials scienceDislocationCrystal twinningGas dynamic cold sprayCopperDeformation (meteorology)Scanning electron microscopeMetallurgyCrystallographyComposite materialChemistry

Abstract

fetched live from OpenAlex

The electron channeling contrast imaging technique was used to investigate the microstructure of copper coatings fabricated by cold gas dynamic spray. The high velocity impact characteristics for cold spray led to the formation of many substructures, such as high density dislocation walls, dislocation cells, deformation twins, and ultrafine equiaxed subgrains/grains. A schematic model is proposed to explain structure refinement of Cu during cold spray, where an emphasis is placed on the role of dislocation configurations and twinning.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 head0.003
GPT teacher head0.210
Teacher spread0.207 · 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

Citations35
Published2014
Admission routes1
Has abstractyes

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