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Record W2072076513 · doi:10.1002/xrs.1208

Optimal <i>K</i> <sub>α</sub> XRF detection geometry of arsenic in skin using an extended fundamental parameter method

2009· article· en· W2072076513 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueX-Ray Spectrometry · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicX-ray Spectroscopy and Fluorescence Analysis
Canadian institutionsMount Allison University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCollimatorDetectorSIGNAL (programming language)PhotonPhysicsOpticsOrientation (vector space)GeometryMathematicsComputer science

Abstract

fetched live from OpenAlex

Abstract The fundamental parameter (FP) method was extended to account for the geometrical details of experimental x‐ray fluorescence (XRF) detection. In the traditional FP method the primary fluorescence photons have parallel pathways towards the detector. In the new approach the primary fluorescence photons can travel in any direction which allows them to reach the detector if not absorbed or scattered. The derived XRF signal equation explicitly depends on the length of the collimator in front of the detector, the detector size, position and orientation. An algorithm which numerically calculates the XRF signal for any set of parameters was developed and implemented for the K α XRF signal of arsenic in skin. Optimal positions and orientations of the detector and collimator ensemble which maximize the XRF signal were found. Results and limitations of the method were also discussed. Copyright © 2009 John Wiley &amp; 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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.281
Teacher spread0.270 · 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