Comparison of Calcite to Quicklime for Amending Partially Oxidized Sulfidic Mine Tailings before Flooding
Bibliographic record
Abstract
Flooding partially oxidized mine tailings for the purpose of mitigating further oxidation of sulfide minerals and generation of acid drainage is generally preceded by treatment with alkaline amendments to prevent releasing previously accumulated acidity to the water cover. This work compares the ability of calcite (CaCO 3 ) and quicklime (CaO), two common amendments, to establish and maintain pH conditions and dissolved metal concentrations within environmentally acceptable ranges over long time periods. Although higher initial pH values were obtained with quicklime, the pH of quicklime treated tailings decreased over time. This was attributed to the low buffering capacity of quicklime treated tailings and to the consumption of hydroxide ions by incongruent dissolution of water-insoluble iron oxyhydroxysulfate minerals. In contrast, the pH of tailings treated with calcite increased initially and then remained stable at pH ≈ 6.7. This pH behavior was due to the lower reactivity of iron oxyhydroxysulfates with calcite, the increased buffering capacity provided by bicarbonate ions, and the incomplete dissolution of calcite. Overall, calcite was found preferable to quicklime for maintaining long-term neutral pH conditions in the treated tailings. With the exception of zinc, acceptable dissolved metal concentrations were achieved with calcite treated 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".