Determination of macro‐ and microelements concentrations by wavelength‐dispersive X‐ray fluorescence spectrometry in the objects of the technogenic ecosystem
Bibliographic record
Abstract
Abstract The wavelength‐dispersive X‐ray fluorescence (WDXRF) spectrometry was applied to determine Na, Mg, Al, P, S, K, Ca, Cr, Mn, Fe, Ni, Cu, Zn, Sr, Ba, and Pb concentrations in the needles of dwarfish spruce Picea canadensis conica and the blue prickly spruce Picea pungens glauca , as well in technogenic soil of Irkutsk city, in which these spruce species grow. All measurements were performed in vacuum using WDXRF spectrometer S8 TIGER produced by Bruker AXS, Germany. The calculated values of instrumental limit of detection (ILD) ranged from 0.5 for Ni to 67 mg/kg for Al for soil certified reference materials (CRMs) such as OOKO151 (light chestnut soil) and from 0.2 for Cu and Ni to 18–24 mg/kg for Na for the plant CRMs such as LB‐1 (birch leaf) and EC‐1 (Canadian waterweed). The repeatability is satisfactory. The values of relative standard deviations (RSDs) do not exceed 15% for soils and available plant material. With calculated T‐statistics, it was found that the WDXRF data do not contain systematic errors. The values of relative discrepancy for WDXRF, total reflection X‐ray fluorescence (TXRF) spectrometry, and atomic emission spectrometry with arc discharge (d.c. arc‐AES) results do not exceed 30% for studied elements.
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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) | 0.000 | 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".