MétaCan
Menu
Back to cohort
Record W3206657600 · doi:10.1155/2021/8070721

Study of the Peak to Background (P/B) Method Behavior as a Function of Take-Off Angle, Tilt Angle, Particle Size, and Beam Energy

2021· article· en· W3206657600 on OpenAlexafffund
Seyed Mahmoud Bayazid, Yu Yuan, Raynald Gauvin

Bibliographic record

VenueScanning · 2021
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsObject Research Systems (Canada)McGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsTilt (camera)Beam (structure)Monte Carlo methodParticle (ecology)Materials scienceOpticsIntensity (physics)Particle sizeMolecular physicsAtomic physicsPhysicsChemistryGeometryMathematicsStatistics

Abstract

fetched live from OpenAlex

Monte Carlo simulations were performed to investigate the behavior of the peak to background ratio (P/B) of particles on a substrate as a function of different variables such as take-off angle, tilt angle, particle size, and beam energy. The results showed that the P/B highly depends on the beam energy, the size of particles, and the composition of the substrates. Results showed that the rate of intensity reduction of the peak is less than the background for a high tilt angle (60 degrees), and thereby, the P/B increases at a high tilt angle. It was shown that by increasing the take-off angle, the P/B initially reduces and then reaches a plateau. Results showed that the P/B highly depends on the size of particles. Analyses showed that by moving the electron beam from the center to the side of the particle, the P/B increases. Finally, the spherical particles have higher sensitivity of the P/B to the beam position than the cubical particles.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.321
Teacher spread0.297 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueScanningSame topicElectron and X-Ray Spectroscopy TechniquesFrench-language works237,207