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Record W2789274815 · doi:10.1002/jemt.23006

Evaluation of electron tomography reconstruction methods for interface roughness measurement

2018· article· en· W2789274815 on OpenAlexaff
Misa Hayashida, Shinichi Ogawa, Marek Malac

Bibliographic record

VenueMicroscopy Research and Technique · 2018
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsUniversity of AlbertaNational Institute for Nanotechnology
Fundersnot available
KeywordsTilt (camera)Surface finishAlgebraic Reconstruction TechniqueIterative reconstructionTomographyElectron tomographyInterface (matter)Materials scienceSurface roughnessProjection (relational algebra)OpticsComputer scienceBiomedical engineeringMathematicsComputational physicsPhysicsAlgorithmGeometryComputer visionScanning electron microscopeComposite materialEngineering

Abstract

fetched live from OpenAlex

We evaluate the suitability of simultaneous iterative reconstruction technique (SIRT), filtered back projection, and simultaneous algebraic reconstruction technique methods for buried interface roughness measurements. We also investigate the effect of total electron dose distributed over the entire tilt series on measured roughness values. We investigate the applicability of the dose fractionation theorem by evaluating the effect of an increasing number of images, i.e., decreasing tilt increment size at fixed total electron irradiation dose on the quantitative measurement of buried interface roughness. The results indicate that SIRT is the most suitable method for reconstruction and a 3° to 5° angle is optimal for the roughness measurement.

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 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)0.024
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: none
Teacher disagreement score0.342
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.101
GPT teacher head0.486
Teacher spread0.384 · 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 teacher head, 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

Citations6
Published2018
Admission routes1
Has abstractyes

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