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Record W2892772183 · doi:10.1107/s160057751801247x

Implications of X-ray beam profiles on qualitative and quantitative synchrotron micro-focus X-ray fluorescence microscopy

2018· article· en· W2892772183 on OpenAlexafffund
Alexander P. Morrell, J. Frederick W. Mosselmans, Kalotina Geraki, Konstantin Ignatyev, Hiram Castillo‐Michel, Peter Monksfield, Adrian Warfield, Maria Febbraio, Helen M. Roberts, Owen Addison, Richard A. Martin

Bibliographic record

VenueJournal of Synchrotron Radiation · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Synchrotron Radiation FacilityDiamond Light Source
KeywordsSynchrotronSynchrotron radiationOpticsBeam (structure)MicroscopyX-ray fluorescenceMaterials scienceMicroscopeX-rayThresholdingFluorescencePhysicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Synchrotron radiation X-ray fluorescence microscopy is frequently used to investigate the spatial distribution of elements within a wide range of samples. Interrogation of heterogeneous samples that contain large concentration ranges has the potential to produce image artefacts due to the profile of the X-ray beam. The presence of these artefacts and the distribution of flux within the beam profile can significantly affect qualitative and quantitative analyses. Two distinct correction methods have been generated by referencing the beam profile itself or by employing an adaptive-thresholding procedure. Both methods significantly improve qualitative imaging by removing the artefacts without compromising the low-intensity features. The beam-profile correction method improves quantitative results but requires accurate two-dimensional characterization of the X-ray beam profile.

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)0.012
metaresearch head score (Gemma)0.028
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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
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 head0.017
GPT teacher head0.352
Teacher spread0.336 · 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
GenreMethods

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
Published2018
Admission routes2
Has abstractyes

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