Prediction, enrichment and isolation identify a responsive, competitive community of cellulolytic microorganisms from a municipal landfill
Bibliographic record
Abstract
Landfills are engineered, heterogeneously contaminated sites containing large reservoirs of paper waste. Cellulose degradation is an important process within landfill microbial ecology, and these anoxic, saturated environments are prime locations for discovery of cellulases that may offer improvements on industrial cellulose degradation efforts. We sampled leachate from three locations within a municipal landfill, a leachate collection cistern, and groundwater from an adjacent aquifer to identify cellulolytic populations and their associated cellulases. Metagenomic sequencing identified wide-spread and taxonomically diverse cellulolytic potential, with a notable scarcity of predicted exocellulases. 16S rRNA amplicon sequencing detected nine landfill microorganisms enriched in a customized leachate medium amended with microcrystalline cellulose or common paper stocks. Paper-enrichment cultures showed competition dynamics in response to the specific composition (lignin: hemi-cellulose: cellulose) of the different paper stocks. From leachate biomass, four novel cellulolytic bacteria were isolated, including two with the capacity for cellulolysis at industrially relevant temperatures. None of the isolates demonstrated exocellulase activity, consistent with the metagenome-based predictions. However, there was very little overlap between metagenome-derived predicted cellulolytic organisms, organisms enriched on paper sources, or the isolates, suggesting the landfill cellulolytic community is at low abundance but able to rapidly respond to introduced substrates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".