Application of powder X-ray diffraction and the Rietveld method to the analysis of oxidation processes and products in sulphidic mine tailings
Bibliographic record
Abstract
The main strength of the Rietveld method as a tool for quantitative phase analysis lies in its ability to control and correct various parameters which influence powder diffraction patterns. The main sources of errors in this type of analysis are due to differences in properties of various constituents of a mineral mixture and the inability to model these consistently. Sources and products of acid mine drainage represent a system where these differences are especially prominent and therefore pose a problem for quantitative phase analysis. Large differences in hardness, mineral habit, and absorption of X-rays plus frequent amounts of poorly crystalline and amorphous components are the main reasons for erroneous results in this kind of samples. We have used a combination of procedures to overcome these difficulties: stepwise grinding of samples to avoid amorphisation of very soft components in the presence of very hard ones, preparation of moderately pressed samples to diminish porosity and surface roughness, application of numerical corrections for the absorption contrast and preferred orientation and the application of a specific addition method for the determination of unknown components in the mixture. The procedures should be tested on synthetic mixtures for each specific instrument/laboratory and type of analysis, as a standard part of the quantitative phase analysis and the necessary way for the estimation of expected errors. In our case mixtures of pyrite, dolomite, gypsum and amorphous silica were applied as proper models. The procedures developed on test mixtures result in estimated errors of determination typically lower than 2 wt%. The approach was applied to the analysis of the complex problem of estimating mass balance in oxidized mine tailings from the Nanisivik Mine, Nunavut, Canada revealing an oxidized proportion of 9 % of total pyrite content in the well drained tailings during a period of 8 years and a negligible oxidation in the water-covered tailings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".