Mitigation of alkaline mine drainage in a natural wetland system
Bibliographic record
Abstract
Many studies have focused on the generation and mitigation of acidic drainage generated when sulfide bearing material is exposed to the atmosphere and undergoes oxidation. Neutral or alkaline mine drainage can be produced from mining waste containing little or no sulfides, and such drainage can also contain elevated metal concentrations, potentially impacting receiving environments. The goal of this study was to characterize the biogeochemical interactions occurring throughout a natural wetland located in the Farr Creek drainage area in Cobalt Canada and to evaluate the ability of the system to effectively attenuate alkaline mine drainage. The biological characterization of the sediment samples demonstrated the presence of acid producing bacteria in consistent numbers with sulphate reducing bacteria and iron reducing bacteria. The data suggested that the acid produced by these active bacterial populations was immediately neutralized by the dissolution of carbonate minerals within the tailings, yielding a neutral to alkaline drainage. The distribution of metals including As, Co, Cu, and Zn throughout the sediments, pore water, and vegetation samples collected at various core locations the metal mass was primarily bound in the sediments or adsorbed onto organic matter or oxide fractions of the sediments. Phytoremediation processes involving Typha latifolia were shown to attenuate metals, particularly Cu and Zn. Adsorption onto organic matter and oxides was another attenuation pathway that significantly improved metal retention. Iron and sulfate reduction were also found to lead to the formation of metal sulfide precipitates, thereby immobilizing the metals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".