Characterization of Indian <i>Ayurvedic</i> herbal medicines for their metal concentrations using WD‐XRF spectrometry
Bibliographic record
Abstract
Abstract This article describes the details of metal concentrations evaluated using wavelength dispersive X‐ray fluorescence (WD‐XRF) spectrometry. A total of 22 elements, Na, Mg, Al, Si, P, S, K, Ti, Ca, Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, Hg, Pb, Ba, Au, and Sn from 16 Ayurvedic medicines were characterized. The method was validated by analyzing the six certified reference materials of soil standards [NIST SRM‐2710, CRM 027‐050 (US‐EPA certified), PS‐1, TILL‐1 and TILL‐4 (Canadian certified reference material, CCRMP) and JSO‐1 (Japanese certified reference material)]. The elemental concentrations in all the standards are found to be within ± 10% of the reported values. Crystalline phases in the individual drug samples were explained by powder X‐ray diffraction (XRD) technique. Qualitative phase identification was done using the ICDD database. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".