Network analysis reveals microbe-mediated impacts of aeration on deep sediment layer microbial communities
Bibliographic record
Abstract
Over-aeration is a common remediation strategy for black and odorous water bodies, in which oxygen is introduced to impact aquatic microbial communities as an electron acceptor of high redox potential. In this study, black-odorous freshwater sediments were cultured for 9 weeks under aeration to investigate microbial covariations at different depths and time points. Based on community 16S rRNA gene sequencing, the microbial covariations were visualized using phylogenetic microbial ecological networks (pMENs). In the spatial scale, we identified smaller and more compact pMENs across all layers compared with the anaerobic control sediments, in terms of network size, average node connectivity, and modularity. The aerated middle layer had the most connectors, the least module hubs, a network hub, shorter average path length, and predominantly positive covariations. In addition, a significant sulfate accumulation in the aerated middle layer indicated the most intense sulfide oxidation, possibly because aeration prompted sediment surface Desulfobulbaceae, known as cable bacteria, to reach the middle layer. In the time scale, similarly, aeration led to smaller pMEN sizes and higher portions of positive covariations. Therefore, we conclude that elevated dissolved oxygen at the water-sediment interface may impact not only the surface sediment but also the subsurface and/or deep sediment microbial communities mediated by microorganisms, particularly by Desulfobulbaceae.
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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 | 0.001 |
| 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 | 0.001 |
| 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".