A metagenomic analysis of tailings microbial communities from both cold and hot environments
Bibliographic record
Abstract
Mining practices produce a substantial waste product in the form of tailings, a problematic liability particularly in materials where iron and sulfur become oxidized leading to acid mine drainage (AMD). Native microbial consortia in tailings sites accelerate this oxidation by a factor of 106. The oxidative capabilities of these consortia can be harnessed to bioleach low-grade, refractory metals from the waste materials while also allowing for the potential stabilization of nuisance elements. This project explores the contributions of native microbes isolated from sulfide tailings from two different climates: colder climate tailings around Sudbury, Ontario and warmer climate arsenopyrite refractory gold tailings from Ecuador (ECT). The cold community project encountered technical challenges as is summarized here as an appendix. This thesis focuses primarily on the ECT community. The ECT tailings were enriched in medium ahead of bioleaching trials and a metagenomic analysis was performed to identify key organisms responsible for driving bioleaching. The main contributors to the ECT system at the order level were Acidithiobacillales, Bacillales, Burkholderiales, Clostridiales, and Thermoplasmatales. The dominant organisms representing these orders were found to have complementary genetic systems that drive iron and sulfur oxidation. Understanding these key players will help optimize the conditions that the ECT culture will be applied in using stirred-tank bioreactors and will provide the baseline metagenomic information to help monitor the health of these organisms throughout bioleaching campaigns.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".