Development and Calibration of a Quantitative, Automated Mineralogical Assessment Method Based on SEM-EDS and Image Analysis: Application for Fine Tailings
Bibliographic record
Abstract
Quantitative mineralogy has seen significant developments from the combination of scanning electron microscopy (SEM) with automatic image analysis and energy dispersive X-ray spectrometry (EDS).The mining industry is one of the fields that has benefited from this progress.In this paper, the authors present a newly developed quantitative method based on SEM-EDS and image analysis (IA), which is used to determine the mineralogical and environmental characteristics of mine tailings.The main objectives of the method are to be able to characterize sulphides and carbonates as monomineral particles, which control the acid generation from the tailings.Pure sulphides, calcite and quartz were blended to make mineralogical standards that represent typical mine tailings environmental behavior.The SEM-EDS-IA method achieved good mineralogical precision for medium (1-20 Wt%) and abundant (> 20 Wt%) minerals, with a relative error below 10 %.However, some corrections had to be applied to account for typical stereological effects (apparent particle diameter from polished surface) and preparation modes (particle segregation during resin hardening).Particle size analysis was used to calibrate the method and identify the corrections to be applied.Since mineralogical quantifications are based on the area of the observed particles, the most reliable particle size analyses (also obtained from particle area) typically lead to the best mineralogical characterization.However, the SEM based techniques may show some limitations for fine-grained particle quantification (< 10 µm), which required additional corrections.In this article, the technique is described, and it is applied to characterize fine-grained mine tailings with a size-by-size mineralogy (with R. Mermillod-Blondin, M. Benzaazoua, M. Kongolo, et al.Vol.10, No.12 1112 sulphides and carbonates content).These results have been used by the Authors to propose an environmental management strategy for acid generating tailings using desulphurization by flotation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".