Unique microbiome in organic matter–polluted urban rivers
Bibliographic record
Abstract
Approximately half of the global annual production of wastewater is released untreated into aquatic environments, which results in worldwide organic matter pollution in urban rivers, especially in highly populated developing countries. Nonetheless, information on microbial community assembly and assembly-driving processes in organic matter-polluted urban rivers remains elusive. In this study, a field study based on water and sediment samples collected from 200 organic matter-polluted urban rivers of 82 cities in China and Indonesia is combined with laboratory water-sediment column experiments. Our findings demonstrate a unique microbiome in these urban rivers. Among the community assembly-regulating factors, both organic matter and geographic conditions play major roles in determining prokaryotic and eukaryotic community assemblies, especially regarding the critical role of organic matter in regulating taxonomic composition. Using a dissimilarity-overlap approach, we found universality in the dynamics of water and sediment community assembly in organic matter-polluted urban rivers, which is distinctively different from patterns in eutrophic and oligotrophic waters. The prokaryotic and eukaryotic communities are dominated by deterministic and stochastic processes, respectively. Interestingly, water prokaryotic communities showed a three-phase cyclic succession of the community assembly process before, during, and after organic matter pollution. Our study provides the first large-scale and comprehensive insight into the prokaryotic and eukaryotic community assembly in organic matter-polluted urban rivers and supports their future sustainable management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".