Bacterial and Archaeal Diversity in Sulfide-Bearing Waste Rock at Faro Mine Complex, Yukon Territory, Canada
Bibliographic record
Abstract
Acid mine/rock drainage (AMD/ARD) is generated by the microbially-accelerated oxidative dissolution of sulfide minerals in working and abandoned mine sites, and some natural environments. Iron-oxidizing microorganisms (IOM) regenerate the oxidant Fe3+, while sulfur-oxidizing microorganisms (SOM) contribute to AMD/ARD by generating H2SO4 via the oxidation of elemental S and reduced inorganic sulfur compounds. Bacterial and archaeal diversity in 34 samples of sulfide-bearing waste rock recovered from three boreholes at the Faro Mine Complex (Yukon Territory, Canada; Pb/Zn production from 1969 to 1998) was investigated using high-throughput amplicon sequencing of 16S rRNA genes. A majority of the borehole pore water samples had circum-neutral or mildly alkaline pH (6.3 − 8.7), while some had low pH (3.3 − 5.2). Mean relative abundance of prokaryotic SOM/IOM accounted for 3.4% of the total amplicons. The acidophilic genera Alicyclobacillus and Acidithiobacillus were the most abundant sulfur- and iron-metabolizing prokaryotes detected, followed by neutrophilic and moderately acidophilic (non-iron-oxidizing) SOM. Sulfate-reducing bacteria (SRB) were also detected, accounting for 0.6% of total reads. The presence of both acidophilic and neutrophilic prokaryotes catalyzing transformations of sulfur and iron in the same samples suggests development of microenvironments within the waste rock where dynamic biogeochemical transformations of these elements occur.
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 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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".