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Record W3085703604 · doi:10.1107/s1600577520010838

<i>X-ray Spectral Imaging Program: XSIP</i>

2020· article· en· W3085703604 on OpenAlexafffund
Peng Qi, Nazanin Samadi, Dean Chapman

Bibliographic record

VenueJournal of Synchrotron Radiation · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of Saskatchewan
FundersWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Research Council CanadaCanada Foundation for InnovationUniversity of SaskatchewanCanadian Light Source
KeywordsMonochromatorSynchrotron radiationSoftwarePython (programming language)WorkflowSynchrotronComputer scienceOpticsImaging spectroscopyImage processingData processingSpectral imagingComputational sciencePhysicsComputer graphics (images)Artificial intelligenceHyperspectral imagingDatabase

Abstract

fetched live from OpenAlex

Spectral K-edge subtraction imaging and wide-field energy-dispersive X-ray absorption spectroscopy imaging are novel, related, synchrotron imaging techniques for element absorption contrast imaging and element speciation imaging, respectively. These two techniques serve different goals but share the same X-ray optics principles with a bent Laue type monochromator and the same data processing algorithms. As there is a growing interest to implement these novel techniques in synchrotron facilities, Python-based software has been developed to automate the data processing procedures for both techniques. In this paper, the concept of the essential data processing algorithms are explained, the workflow of the software is described, and the main features and some related utilities are introduced.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: Software
Teacher disagreement score0.086
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0860.037

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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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 designNot applicable
Domainnot available
GenreSoftware

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
Published2020
Admission routes2
Has abstractyes

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