Bacterial community assembly in activated sludge: mapping beta diversity across environmental variables
Bibliographic record
Abstract
Effect of ecological variables on community assembly of heterotrophic bacteria at eight full-scale and two pilot-scale activated sludge wastewater treatment plants (AS-WWTPs) were explored by pyrosequencing of 16S rRNA gene amplicons. In total, 39 samples covering a range of abiotic factors spread over space and time were analyzed. A core bacterial community of 24 families detected in at least six of the eight AS-WWTPs was defined. In addition to the core families, plant-specific families (observed at <50% AS-WWTPs) were found to be also important in the community structure. Observed beta diversity was partitioned with respect to ecological variables. Specifically, the following variables were considered: influent wastewater characteristics, season (winter vs. summer), process operations (conventional, oxidation ditch, and sequence batch reactor), reactor sizes (pilot-scale vs. full-scale reactors), chemical stresses defined by ozonation of return activated sludge, interannual variation, and geographical locations. Among the assessed variables, influent wastewater characteristics and geographical locations contributed more in explaining the differences between AS-WWTP bacterial communities with a maximum of approximately 26% of the observed variations. Partitioning of beta diversity is necessary to interpret the inherent variability in microbial community assembly and identify the driving forces at play in engineered microbial 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".