Experimental Validation of Quantitative XANES Analysis for Phosphorus Speciation
Bibliographic record
Abstract
The quantitative approach used in x‐ray absorption spectroscopy (XAS) experiments is oftentimes based on statistical goodness‐of‐fit criteria, which do not explain the accuracy of the components obtained from the fittings. This study was performed to validate the linear combination (LC) approach used in quantitative XAS analysis by estimating the accuracy of this procedure. Near‐edge Kα 1 fluorescence XAS spectra were acquired for known binary mixtures of Ca, Al, and Fe phosphates in varying proportions and for the individual compounds. All combinations of the spectra of model compounds were fitted to the spectra of the known mixtures to obtain their relative abundance. The binary combinations produced the best fit with χ 2 values ranging from 0.02 to 0.25. The relative error associated with the fitting ranged from as low as 0.8 to 17% for thoroughly mixed samples. The relative error was small when the proportion of Ca phosphate in the mixture was high but the error was large at low abundance of this component in the mixture. Because the interpretation of the XANES result largely depends on the relative proportion of species in the sample obtained by LC, we therefore recommend acquiring a spectrum for a mixture of certified reference compounds that mimics the composition of the sample being investigated at the beamline to estimate the accuracy of the proportions obtained from quantitative x‐ray absorption near‐edge structure (XANES) analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".