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Record W3038547128 · doi:10.1017/s1431927600037090

On the Peak to Background Ratio of X-Rays emitted from Rough Surfaces

2000· article· en· W3038547128 on OpenAlexaff
Raynald Gauvin, Eric Lifshin

Bibliographic record

VenueMicroscopy and Microanalysis · 2000
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMicroanalysisMonte Carlo methodPlanarIonizationPhotonMaterials sciencePhoton energyHomogeneousIntensity (physics)Computational physicsOpticsSurface finishAnalytical Chemistry (journal)PhysicsChemistryIonStatisticsMathematicsStatistical physicsComposite material

Abstract

fetched live from OpenAlex

Abstract The classical schemes to convert the x-ray intensity into concentration, using the ZAF or the ϕ (ρz ) methods, are valid for specimens having homogeneous composition and flat surfaces. Quantitative schemes have also been developed for x-ray microanalysis of multi-layered specimens. More recently, a quantitative method has been proposed for the microanalysis of spherical inclusions embedded in a matrix as well as Monte Carlo simulations of x-ray emission from porous materials. For the case of specimens having a non-planar surface, a quantitative method based on the peak to background ratio, using photons of the same energy, has been proposed1. However, this method has some pitfalls. First, this method is based on the assumption that the peak to background ratio is independent of the specimen roughness which is not strictly correct because the ionization cross sections and the bremstrallung cross sections are not the same. Therefore, the shapes of the ϕ (p z ) curves are not the same for characteristic and continuum photon of the same energy resulting in different absorption corrections. The result is that the peak to background ratio will vary with beam position on a rough surface.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.006
Threshold uncertainty score0.995

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)0.0060.000

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 head0.011
GPT teacher head0.270
Teacher spread0.258 · 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.

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

Citations1
Published2000
Admission routes1
Has abstractyes

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