Use of Alkaline Extraction to Quantify Sulfate Concentration in Oxidized Mine Tailings
Bibliographic record
Abstract
An alkaline extraction method has been developed for the determination of total sulfate in mine tailings containing secondary sulfate minerals formed by the oxidation of primary sulfides. Oxidized tailings were extracted with a 0.3 M NaOH solution at a liquid/solid ratio of 30 at room temperature for 16 h. The sulfate concentration in the extracts was determined by ion chromatography (IC). The coefficient of variation for sulfate determinations ranged from 1.9 to 3.2% for five tailings samples collected at two tailings impoundments. Mineralogical analysis of the tailings by scanning electron microscopy/X-ray energy dispersive spectrometry (SEM/EDS) demonstrated that the extraction of sulfate was complete, with the exception of extremely insoluble barite. The proposed method is simple, yields an accurate yet rapid measurement of sulfate, and involves a safer laboratory operation than conventional methods that make use of strong HCl acid solutions. Moreover, this method allows the specific measurement of sulfate in the extract, whereas conventional methods are generally limited to the measurement of total S by inductively coupled plasma atomic emission spectrometry (ICP-AES) due to the interference of chloride with sulfate in IC.
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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) | 0.001 | 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 | 0.000 | 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".