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Record W2984236326 · doi:10.1016/j.simpa.2019.100010

Peakaboo: Advanced software for the interpretation of X-ray fluorescence spectra from synchrotrons and other intense X-ray sources

2019· article· en· W2984236326 on OpenAlexafffund
Lisa L. Van Loon, N. S. McIntyre, Michael Bauer, Nathaniel Sherry, Neil R. Banerjee

Bibliographic record

VenueSoftware Impacts · 2019
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsWestern University
FundersWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaCanadian HIV Trials Network, Canadian Institutes of Health ResearchUniversity of SaskatchewanNational Research Council CanadaCanarieCanada Foundation for InnovationOntario Centres of ExcellenceU.S. Department of Energy
KeywordsX-ray fluorescenceSoftwareSynchrotronSpectral lineTRACE (psycholinguistics)Synchrotron radiationInterpretation (philosophy)Sensitivity (control systems)Computer scienceFluorescenceAnalytical Chemistry (journal)PhysicsOpticsChemistryEngineeringEnvironmental chemistryElectronic engineering

Abstract

fetched live from OpenAlex

Peakaboo is a platform for the analysis of full spectrum synchrotron X-ray fluorescence (XRF) map data. It offers robust, automated fitting of XRF spectral peaks, increasing the sensitivity to trace chemical elements. Large arrays of spectral data can also be processed into element maps to define spatial relationships. The modifications to Peakaboo introduced since 2017 are particularly designed to help new users to become rapidly competent in the handling of complex XRF spectra. Some of the most important innovations of the software are described below. Finally, we share examples of its application in Earth and Environmental Sciences.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.069
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0030.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0690.028

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 head0.008
GPT teacher head0.227
Teacher spread0.220 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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