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Record W3039306064 · doi:10.1017/s1431927600029561

Evaluation of Corrections for X-Ray Microanalysis in the ESEM

2001· article· en· W3039306064 on OpenAlexaff
Robert A. Carlton, Charles E. Lyman, James E. Roberts, Raynald Gauvin

Bibliographic record

VenueMicroscopy and Microanalysis · 2001
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsEnvironmental scanning electron microscopeMicroanalysisScatteringElectronBeam (structure)Scanning electron microscopeCathode rayAnalytical Chemistry (journal)X-rayMaterials scienceOpticsSecondary electronsAtomic physicsChemistryPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Abstract A number of methods have been proposed to correct for the electron beam scattering effects on xray microanalysis in the environmental scanning electron microscope (ESEM). This paper presents an evaluation of two of these methods. The Doehne method is based on the observation that x-ray counts due to the unscattered electron beam increase with decreasing chamber pressure whereas the inverse is true for x-ray counts due to scattered electrons. The x-ray count intercept, at zero pressure, of the regression lines relating x-ray counts to chamber vapor pressure is an estimate of the high-vacuum intensity. The Gauvin method is based on the relationship between x-ray counts and the fraction of the electron beam that is unscattered, fp.The fraction of the unscattered beam is calculated using an equation derived from scattering theory and uses the accelerating voltage, the gas path length, and the chamber vapor pressure.

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)0.008
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.022
GPT teacher head0.330
Teacher spread0.308 · 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 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

Citations0
Published2001
Admission routes1
Has abstractyes

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