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 \nliability particularly in materials where iron and sulfur become oxidized leading to acid mine \ndrainage (AMD). Native microbial consortia in tailings sites accelerate this oxidation by a factor \nof 106. The oxidative capabilities of these consortia can be harnessed to bioleach low-grade, \nrefractory metals from the waste materials while also allowing for the potential stabilization of \nnuisance elements. This project explores the contributions of native microbes isolated from \nsulfide tailings from two different climates: colder climate tailings around Sudbury, Ontario and \nwarmer climate arsenopyrite refractory gold tailings from Ecuador (ECT). The cold community \nproject encountered technical challenges as is summarized here as an appendix. This thesis \nfocuses primarily on the ECT community. The ECT tailings were enriched in medium ahead of \nbioleaching trials and a metagenomic analysis was performed to identify key organisms \nresponsible for driving bioleaching. The main contributors to the ECT system at the order level \nwere Acidithiobacillales, Bacillales, Burkholderiales, Clostridiales, and Thermoplasmatales. The \ndominant organisms representing these orders were found to have complementary genetic \nsystems that drive iron and sulfur oxidation. Understanding these key players will help optimize \nthe conditions that the ECT culture will be applied in using stirred-tank bioreactors and will \nprovide the baseline metagenomic information to help monitor the health of these organisms \nthroughout bioleaching campaigns.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 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".