Toward an intensive understanding of sewer sediment prokaryotic community assembly and function
Bibliographic record
Abstract
Prokaryotic communities play important roles in sewer sediment ecosystems, but the community composition, functional potential, and assembly mechanisms of sewer sediment prokaryotic communities are still poorly understood. Here, we studied the sediment prokaryotic communities in different urban functional areas (multifunctional, commercial, and residential areas) through 16S rRNA gene amplicon sequencing. Our results suggested that the compositions of prokaryotic communities varied significantly among functional areas. Desulfomicrobium, Desulfovibrio, and Desulfobacter involved in the sulfur cycle and some hydrolytic fermentation bacteria were enriched in multifunctional area, while Methanospirillum and Methanoregulaceae, which were related to methane metabolism were significantly discriminant taxa in the commercial area. Physicochemical properties were closely related to overall community changes (p < 0.001), especially the nutrient levels of sediments (i.e., total nitrogen and total phosphorus) and sediment pH. Network analysis revealed that the prokaryotic community network of the residential area sediment was more complex than the other functional areas, suggesting higher stability of the prokaryotic community in the residential area. Stochastic processes dominated the construction of the prokaryotic community. These results expand our understanding of the characteristics of prokaryotic communities in sewer sediment, providing a new perspective for studying sewer sediment prokaryotic community structure.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".