Investigating the spatial heterogeneity impact on the geochemical and mineralogical controlling factors framing acid generation at a closed tailings facility
Bibliographic record
Abstract
Mineral industry is increasingly generating massive amounts of tailings, usually deposited in tailings storage facilities (TSF). To highlight the spatial variability effect on the geoenvironmental outcomes, five samples were collected from a closed TSF from different locations. The characterization findings were used to investigate the heterogeneous physical, geochemical, and mineralogical properties depending on the sample location. For instance, the pyrite content spans the interval between 0.02 and 8 wt%. Likewise, the carbonate content varies from 0.05 wt% to 43 wt%. Static tests performed to assess the potential of acid generation yielded various findings regarding the acid generation potential. Therefore, each static test should be interpreted without overlooking its limitations and the mineralogical implications under the test operating conditions. Hence, a thorough mineralogical characterization was undertaken for the selected samples to highlight their inherent mineralogical differences. Additionally, kinetic testing using weathering cell tests, highlighted that the samples C1 and C2 are acid generating, whereas C3, C4 and C5 maintain circumneutral to alkaline pH throughout the testing period. Iron release was negligible for the samples C2, C3, C4 and C5 due to its precipitation as secondary iron-oxyhydroxides that coated pyrite in circumneutral conditions. A kinetic model was established using PHREEQC to perform a parametric analysis regarding the neutralization lag time of silicates as well as the effect of calcite depletion on iron and sulfate release. The main outcome of the model underline that at least 20 wt% albite should be included in a calcite-based amendment to prevent iron release upon calcite depletion or passivation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".