Selenium Concentrations in Twenty‐Six Geological Reference Materials: New Determinations and Proposed Values
Bibliographic record
Abstract
The interest in selenium concentrations in whole rocks is growing, in part because it is a useful tool for base and precious metal exploration. Selenium is often neglected in whole rock geochemistry because of the inability of most laboratories to make reliable determinations of this element. A consequence of these difficulties is a paucity of assigned or certified values for Se in international geological reference materials, so that the “best practice” proposed by Kane and Potts (2007) to obtain robust values for such reference materials cannot be followed. In order to address this problem, we have determined Se by pre‐concentration on thiol‐cotton fibre followed by INAA (Se/TCF‐INAA technique) in twenty‐six international geological reference materials, and one quality control material (KPT‐1). These values were used, in conjunction with a set of published values, to estimate Se concentrations for these twenty‐seven reference samples. Robust statistics were developed for seven of the RMs, with standard deviations equal to or less than precisions calculated using the Horwitz function and so that consensus values could be proposed. For three of the RMs, the presence of outliers gave less robust results, and suggested values are proposed. For seventeen of the RMs, only information values are provided, because either insufficient determinations were available or because large standard deviations of the data were derived.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".