Diversity of the Sediment Microbial Community in the Aha Watershed (Southwest China) in Response to Acid Mine Drainage Pollution Gradients
Bibliographic record
Abstract
Located in southwest China, the Aha watershed is continually contaminated by acid mine drainage (AMD) produced from upstream abandoned coal mines. The watershed is fed by creeks with elevated concentrations of aqueous Fe (total Fe > 1 g/liter) and SO4 (2-) (>6 g/liter). AMD contamination gradually decreases throughout downstream rivers and reservoirs, creating an AMD pollution gradient which has led to a suite of biogeochemical processes along the watershed. In this study, sediment samples were collected along the AMD pollution sites for geochemical and microbial community analyses. High-throughput sequencing found various bacteria associated with microbial Fe and S cycling within the watershed and AMD-impacted creek. A large proportion of Fe- and S-metabolizing bacteria were detected in this watershed. The dominant Fe- and S-metabolizing bacteria were identified as microorganisms belonging to the genera Metallibacterium, Aciditerrimonas, Halomonas, Shewanella, Ferrovum, Alicyclobacillus, and Syntrophobacter. Among them, Halomonas, Aciditerrimonas, Metallibacterium, and Shewanella have previously only rarely been detected in AMD-contaminated environments. In addition, the microbial community structures changed along the watershed with different magnitudes of AMD pollution. Moreover, the canonical correspondence analysis suggested that temperature, pH, total Fe, sulfate, and redox potentials (Eh) were significant factors that structured the microbial community compositions along the Aha watershed.
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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 | 0.001 | 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) | 0.000 | 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 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".