Efficiency of thermally activated eggshells for acid mine drainage treatment in cold climate
Bibliographic record
Abstract
The selection process for a technology to treat the mine drainage is influenced by its performance, technical feasibility and cost. For the last decades, the mine industry has been searching for green methods to treat the acid mine drainage (AMD). This study evaluated thermally activated eggshells as a potential alternative for AMD treatment in cold climate. It was found that metal removal efficiency depended on the eggshells' calcination temperature and on the solid: liquid ratio. Eggshells calcined at 900 °C and employed in a solid: liquid ratio of 0.3% increased the pH of a mine effluent from 2.5 to 6.9 at 20 °C and to 9.2 at 4 °C, respectively. Heavy metal concentrations were reduced as follows: As <0.0005 mg/L, Cd 0.00022 and 0.00009 mg/L, Cu 0.0061 and 0.0091 mg/L, Co 0.0338 and 0.0016 mg/L, Cr 0.0015 and 0.0022 mg/L, Fe <0.01 mg/L, Ni 0.0291 and 0.0024 mg/L, Mo <0.0005 mg/L, Se 0.0033 and 0.0044 mg/L, and Zn 0.007 and 0.022 mg/L, at 20 °C and 4 °C, respectively. Consequently, the treated mine effluent satisfied both the Quebec and Canadian regulations. Based on these results, thermally activated eggshells could be efficiently employed for the active and passive treatment of AMD in a cold climate.
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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 | 0.000 |
| 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) | 0.001 | 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".