Quantification of Metals and Semimetals in Carbon‐Rich Rocks: A New Sequential Protocol Including Extraction from Humic Substances
Bibliographic record
Abstract
We have developed a new sequential extraction technique that does not require complex procedures and is efficient in determining metal and semimetal contents of carbon‐rich rocks. Six geological reference materials (SBC‐1, SCHS‐1, SCo‐1, SDO‐1, SGR‐1b and SLg‐1) and an in‐house black shale (SH‐1) were selected to test the method, which consists of four main digestion steps involving: (a) dilute HCl acid; (b) NaOH; (c) aqua regia; and (d) hydrofluoric acid. Compared with traditional aqua regia + hydrofluoric acid attack, this new protocol recovers more of the moderately volatile elements during early extraction of humic substances. In addition when compared with reference values, those for most elements are in agreement within uncertainty. Furthermore, this new protocol reveals important information on the partitioning of elements; for instance, steps one and two indicate which elements are associated with carbonates and organic phases from fulvic and humic acid extractions, whereas step three provides results for which elements are associated with sulfide minerals and step four indicates which elements remained in the silicate and oxide phases.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".