Microbial community origin and fate through a rural wastewater treatment plant
Bibliographic record
Abstract
Conventional wastewater treatment relies on a complex microbiota; however, much of this community is still to be characterized. To better understand the origin, dynamics and fate of bacteria within a wastewater treatment plant: untreated primary wastewater, activated sludge and post-treatment effluent were characterized. From 3163 exact sequence variants (ESVs), 860 were annotated to species-level. In primary wastewater, 28% of ESVs were putative bacterial species previously associated with humans, 14% with animals and 5% as common to the environment. Differential abundance analysis revealed significant relative reductions in ESVs from potentially human-associated species from primary wastewater to activated sludge, and significant increases in ESVs from species associated with nutrient cycling. Between primary wastewater and effluent, 51% of ESVs from human-associated species did not significantly differ, and species such as Bacteroides massiliensis and Bacteroides dorei increased. These findings illustrate that activated sludge increased extracellular protease and urease-producing species, ammonia and nitrite oxidizers, denitrifiers and specific phosphorus accumulators. Although many human-associated species declined, some persisted in effluent, including strains of potential health or environmental concern. Species-level microbial assessment may be useful for understanding variation in wastewater treatment efficiency as well as for monitoring the release of microbes into surface water and the wider ecosystem.
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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".