High diversity and heterogeneity define microbial communities across an active municipal landfill
Bibliographic record
Abstract
Abstract Global waste production is increasing rapidly, with the majority of waste destined for landfills. Microbial communities in landfills transform waste and generate methane in an environment unique from other built and natural environments. Previous work has largely considered landfill microbial diversity only at the phylum level, identifying complex and variable communities. The extent of shared organismal diversity across landfills or over time and at more precise levels of classification remains unknown. We used 16S rRNA gene amplicon and metagenomic sequencing to examine the taxonomic and functional diversity of the microbial communities inhabiting a Southern Ontario landfill. The diversity of microbial populations in leachate and groundwater samples was correlated with geochemical conditions to determine drivers of microbial heterogeneity. Across the landfill, 25 bacterial and archaeal phyla were present at >1% relative abundance within at least one landfill sample. The Patescibacteria , Bacteroidota , Firmicutes , and Proteobacteria had the highest relative abundances, with most other phyla present at low (<5%) abundance. Below the phylum level, very few populations were identified at multiple sites, with only 121 of 8,030 populations present at five or more sites. This indicates that, although phylum-level signatures are conserved, individual landfill microbial populations vary widely. Significant differences in geochemistry occurred across the leachate and groundwater wells sampled, with calcium, iron, magnesium, boron, meta and para xylenes, ortho xylenes, and ethylbenzene concentrations contributing most strongly to observed site differences. This study illustrates that leachate microbial communities are much more complex and diverse within landfills than previously reported, with implications for waste management best practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".