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Record W565046408 · doi:10.1107/s1600576715005658

Rotation contour contrast reconstruction using electron backscatter diffraction in a scanning electron microscope

2015· article· en· W565046408 on OpenAlexaff
Shirin Kaboli, Hendrix Demers, Nicolas Brodusch, Raynald Gauvin

Bibliographic record

VenueJournal of Applied Crystallography · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsMcGill University
FundersSociety for Analytical Chemists of Pittsburgh
KeywordsElectron backscatter diffractionRotation (mathematics)OpticsScanning electron microscopeElectron microscopeMicrographMaterials scienceZone axisElectronCrystal (programming language)DiffractionKikuchi lineElectron diffractionContrast (vision)Cathode rayPhysicsReflection high-energy electron diffractionGeometryMathematics

Abstract

fetched live from OpenAlex

The microstructure of a deformed Mg–Al–Ca alloy was imaged using an electron-beam energy of 20 keV in a cold field-emission scanning electron microscope. The backscattered electron (BSE) micrographs showed a non-uniform contrast, the simplest being in the form of parallel contours inside a number of grains. This contrast is described as rotation contour contrast (RCC) and is attributed to local rotation of the crystal during the deformation of the grain. A model is presented to relate the rotation of crystal planes about one rotation axis to the channeling contrast in the channeling pattern and, consequently, to RCC in the BSE micrograph. This model was validated with the electron backscatter diffraction technique such that the RCCs in the BSE micrograph were reconstructed using the electron backscatter diffraction pattern intensities. The appearance of the RCCs was attributed to the change in the electron-beam position across a Kikuchi band due to local crystal rotation.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.252
Teacher spread0.235 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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Same venueJournal of Applied CrystallographySame topicMagnesium Alloys: Properties and ApplicationsFrench-language works237,207