Next-generation sequencing showing potential leachate influence on bacterial communities around a landfill in China
Bibliographic record
Abstract
The impact of contaminated leachate on groundwater from landfills is well known, but the specific effects on bacterial consortia are less well-studied. Bacterial communities in a landfill and an urban site located in Suzhou, China, were studied using Illumina high-throughput sequencing. A total of 153 944 good-quality reads were produced and sequences assigned to 6388 operational taxonomic units. Bacterial consortia consisted of up to 16 phyla, including Proteobacteria (31.9%–94.9% at landfill, 25.1%–43.3% at urban sites), Actinobacteria (0%–28.7% at landfill, 9.9%–34.3% at urban sites), Bacteroidetes (1.4%–25.6% at landfill, 5.6%–7.8% at urban sites), Chloroflexi (0.4%–26.5% at urban sites only), and unclassified bacteria. Pseudomonas was the dominant (67%–93%) genus in landfill leachate. Arsenic concentrations in landfill raw leachate (RL) (1.11 × 103 μg/L) and fresh leachate (FL2) (1.78 × 103 μg/L) and mercury concentrations in RL (10.9 μg/L) and FL2 (7.37 μg/L) exceeded Chinese State Environmental Protection Administration standards for leachate in landfills. The Shannon diversity index and Chao1 richness estimate showed RL and FL2 lacked richness and diversity when compared with other samples. This is consistent with stresses imposed by elevated arsenic and mercury and has implications for ecological site remediation by bioremediation or natural attenuation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".