ICP-MS AND ICP-AES ANALYSIS OF PLANT REFERENCE MATERIALS
Bibliographic record
Abstract
Two methods — ICP-MS and ICP-AES are used for certification of the new reference material — needles of Siberian pine (NSP-1). Techniques of the analysis include decomposition of plant samples in two different ways: acid digestion in a microwave system MARS-5 and lithium metaborate fusion followed by ICP-MS and ICP-AES analysis of the solutions. Simultaneous determinations of all the elements were carried out in low, medium and high resolution using SF-mass-spectrometer ELEMENT and atomic-emission spectrometer IRIS Advantage with external calibrations and internal standards (In — ICP-MS, Sc —ICP-AES). Middle and high resolutions of ICP mass spectrometer were used for interference corrections. Data obtained by ICP-MS and ICP-AES with different decomposition techniques are in good agreement. The ICP-MS and ICP-AES techniques have been validated by the analysis of three plant reference materials: LB-1 (leaf of a birch), Tr-1 (grass mixture) and EK-1 (Canadian pondweed). These techniques were used for the determination of 38 elements in the new reference material NSP-1. Relative standard deviations for most of the determined elements were below 10%. Combination of ICP-MS and ICP-AES techniques for certification of the new reference material makes it possible to expand the set of elements to be determined and to reduce the total analysis time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".