Efficacy of lime mud residues from kraft mills to amend oxidized mine tailings before permanent flooding
Bibliographic record
Abstract
Flooding mine tailings that have partially oxidized is a rehabilitation method commonly practiced at sites having a positive hydrologic balance and a suitable topography to limit further oxidation of the remaining sulfide minerals. The purpose of this study was to assess the efficacy of amending oxidized mine tailings with lime mud residue (LMR) before flooding to reduce the release of acidity and heavy metals to the water cover and the pore water. Series of LMR samples from three kraft pulp mills were collected over several weeks and characterized to evaluate their variability in composition and physical characteristics. All LMR samples consisted of silt-sized aggregates of CaCO 3 crystals and contained very low concentrations of legislated heavy metals. Flow-through column tests were carried out with the following three configurations: (i) water cover over oxidized tailings; (ii) water cover over LMR layer over oxidized tailings; and (iii) water cover over LMR + oxidized tailings mixtures over oxidized tailings. A permanent water cover was maintained at the top of all the columns to simulate flooded conditions. The LMR amendment was effective for maintaining the pH at neutral or slightly alkaline values and dissolved heavy metal concentrations below regulatory limits in the water cover and in the pore water of the mixed LMR + tailings layer. However, the pH remained acidic and heavy metal concentrations were elevated in the pore water of the underlying oxidized tailings layer, even after hundreds of pore volumes of water had infiltrated through the columns. The behavior of pore water pH in the underlying oxidized tailings was attributed to the low solubility of CaCO 3 and the consumption of bicarbonate ions by reaction with Fe-oxyhydroxysulfate minerals. Key words: lime mud residue, recaust, tailings, acid mine drainage, flooding, water cover.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".