Glucose-induced changes in the bacterial communities of mine tailings at different acidification stages
Bibliographic record
Abstract
Ecological restoration technologies applied to tailings can influence the associated bacterial communities. However, it is unknown if the shifts in these bacterial communities are caused by increased organic carbon. Glucose-induced respiration and high-throughput sequencing were used to assess the microbial activity and bacterial communities, respectively. Glucose addition increased the microbial activity, and glucose + ammonium nitrate addition resulted in slightly higher CO2 emission than did glucose addition alone, suggesting that carbon and nitrogen limited microbial community growth. In neutral pH tailings, the bacterial taxa that increased by glucose addition were assigned to the phyla Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes, Firmicutes, and Planctomycetes. However, the bacterial taxa that increased by glucose addition in acidic tailings only belonged to the phylum Actinobacteria (maximum increase of 43.78%). In addition, the abundances of the total nitrogen-fixing genera and of the genus Arthrobacter (representing approximately 97.89% of the total nitrogen-fixing genera) increased by glucose addition in acidic tailings (maximum increase of 46.98%). In contrast, the relative abundances of the total iron- and (or) sulfur-oxidizing bacteria decreased (maximum decrease of 10.41%) in response to the addition of glucose. These findings indicate that the addition of organic carbon is beneficial to the development of bacterial communities in mine tailings.
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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) | 0.001 | 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".