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Record W2095802323 · doi:10.1017/s1431927612000207

Spatial Resolution Optimization of Backscattered Electron Images Using Monte Carlo Simulation

2012· article· en· W2095802323 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

VenueMicroscopy and Microanalysis · 2012
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsUniversité de SherbrookeMcGill University
Fundersnot available
KeywordsMonte Carlo methodElectronImage resolutionComputational physicsResolution (logic)Statistical physicsPhysicsOpticsMaterials scienceComputer scienceArtificial intelligenceMathematicsNuclear physicsStatistics

Abstract

fetched live from OpenAlex

The relation between probe size and spatial resolution of backscattered electron (BSE) images was studied. In addition, the effect of the accelerating voltage, the current intensity and the sample geometry and composition were analyzed. An image synthesis method was developed to generate the images from backscattered electron coefficients obtained from Monte Carlo simulations. Spatial resolutions of simulated images were determined with the SMART-J method, which is based on the Fourier transform of the image. The resolution can be improved by either increasing the signal or decreasing the noise of the backscattered electron image. The analyses demonstrate that using a probe size smaller than the size of the observed object (sample features) does not improve the spatial resolution. For a probe size larger than the feature size, the spatial resolution is proportional to the probe size.

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 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 score0.227
Threshold uncertainty score0.767

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)0.0000.000

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 head0.012
GPT teacher head0.296
Teacher spread0.283 · 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