Changes in microbial communities of a passive coal mine drainage bioremediation system
Bibliographic record
Abstract
Drainage from abandoned mines is one factor greatly affecting the streams and vegetation in and around Pittsburgh and the Appalachian Mountains where coal mining occurred. This drainage may be more acidic, alkaline, or metal based. Different methods for remediation exist. Passive remediation is one method used to naturally allow the metals to precipitate out and aid in cleaning up the water. The goal of this study is to sample different holding ponds in a sequential passive remediation system and determine microbial communities present at each site of an abandoned coal mine drainage site. Sequencing of the 16S rRNA gene of the sediment indicated the most abundant phyla at each of the 5 ponds and wetland area included Proteobacteria (36%–43%), Bacteroidetes (12%–37%), Firmicutes (3%–11%), and Verrucomicrobia (6%–11%). Analysis of genera between the first, and most polluted, pond included Solitalea, Pedosphaera, and Rhodocyclus, whereas the microbial community from the wetland site at the end of the remediation system included Ignavibacterium, Pelotomaculum, and Petrimonas. The results of our microbial community composition study of sediment from a passive treatment system are in line with organisms commonly found in sediment regardless of iron oxide precipitation, while others are preferentially found in the less polluted wetland site.
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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 | 0.000 | 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 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".