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Record W2286499979 · doi:10.1017/s1431927615015603

Monte Carlo Simulations of Electron Energy-Loss Spectra with the Addition of Fine Structure from Density Functional Theory Calculations

2016· article· en· W2286499979 on OpenAlexaff
M. Attarian Shandiz, Maxime J.‐F. Guinel, Majid Ahmadi, Raynald Gauvin

Bibliographic record

VenueMicroscopy and Microanalysis · 2016
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsMcGill University
FundersNational Science Foundation
KeywordsMonte Carlo methodIonizationSpectral lineRange (aeronautics)ElectronAtomic physicsComputational physicsDensity functional theoryOscillator strengthScatteringEnhanced Data Rates for GSM EvolutionMolecular physicsMaterials scienceElectron scatteringPhysicsOpticsIonQuantum mechanics

Abstract

fetched live from OpenAlex

A new approach is presented to introduce the fine structure of core-loss excitations into the electron energy-loss spectra of ionization edges by Monte Carlo simulations based on an optical oscillator model. The optical oscillator strength is refined using the calculated electron energy-loss near-edge structure by density functional theory calculations. This approach can predict the effects of multiple scattering and thickness on the fine structure of ionization edges. In addition, effects of the fitting range for background removal and the integration range under the ionization edge on signal-to-noise ratio are investigated.

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)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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