Determination of Te, As, Bi, Sb and Se (TABS) in Geological Reference Materials and Geo<i>PT</i> Proficiency Test Materials by Hydride Generation‐Atomic Fluorescence Spectrometry (HG‐AFS)
Bibliographic record
Abstract
The study of Te, As, Bi, Sb and Se (TABS) has increased over the past years due to their use in the development of low‐carbon energy technologies. However, there is a scarcity of mass fraction values of TABS in geological reference materials. This underlines the difficulty in undertaking routine determinations of these elements. The mass fractions of TABS were determined in geological reference materials using hydride generation‐atomic fluorescence spectrometry (HG‐AFS), calibrated with standard solutions. Comparisons with literature values were used to validate the method. Samples from the GeoPT proficiency test were also analysed. For most elements, there are no assigned or even provisional values for many of the GeoPT and reference materials because of the wide range of results reported. For mass fractions above the quantification limit of the method, our results are in good agreement with the median of GeoPT results. Thus, we propose GeoPT median values as informational values for these elements. In contrast, at mass fractions < 0.5 µg g−1 median values of Se from GeoPT are systematically higher than our results. Our Se results are in agreement with the reference materials down to 0.02 µg g−1, which suggest that many of the results for Se reported in GeoPT testing are too high.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".