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Record W1563720373 · doi:10.1002/xrs.2583

Choice of X‐ray mass attenuation coefficients for PIXE analysis of silicate minerals and rocks

2015· article· en· W1563720373 on OpenAlexafffund
Christopher M. Heirwegh, Irina Pradler

Bibliographic record

VenueX-Ray Spectrometry · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicX-ray Spectroscopy and Fluorescence Analysis
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsAttenuationSilicateAnalytical Chemistry (journal)Mass attenuation coefficientRange (aeronautics)Attenuation coefficientComputationMineralogyComputational physicsMaterials sciencePhotonAtomic physicsChemistryPhysicsOpticsMathematicsComposite materialAlgorithm

Abstract

fetched live from OpenAlex

The accuracy of each of three mass attenuation coefficient databases was assessed using particle‐induced X‐ray emission measurements performed on well‐characterized, homogeneous silicate glass and mineral standards. In a fundamental parameters computation, the absolute efficiency constants of the light elements Mg, Al and Si, found within each standard, were determined. These were compared with the efficiency constants deduced from separate particle‐induced X‐ray emission measurements performed on pure targets of the same three elements. In this comparison, a 7–9% discrepancy was found when using the XCOM database for the computation, but this was reduced to 2–5% when FFAST coefficients were substituted. Further improvement was achieved when a hybrid database was adopted. This ‘Mixed’ database consisted of primarily XCOM coefficients with FFAST values inserted for the light element ( Z = 11,…, 14) attenuation of photons having energy less than their K edge and for oxygen in the 1–2 keV range. The average efficiency constant discrepancies were reduced to −0.5–2%. Copyright © 2015 John Wiley & Sons, Ltd.

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)0.004
metaresearch head score (Gemma)0.010
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.297
Teacher spread0.274 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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