Organic Matter Decomposition in River Ecosystems: the Role of Microbial Interactions Regulated by Environmental Factors
Bibliographic record
Abstract
Abstract Microbes are the critical contributors to the organic matter decomposition (OMD) in river ecosystems. However, the role of microbial interactions on the OMD in river ecosystems and the regulation of environmental factors to the microbial interactions were not considered previously thus tacked in this study. Cotton strip (CS) as a substitute for organic matter was introduced to Luanhe River Basin in China. The results indicated that CS selectively enriched bacterial and fungal groups related to cellulose decomposition, leading to the cotton strip decomposition (CSD). In these groups, bacterial phyla Proteobacteria, fungal phyla Rozellomycota and Ascomycota were the dominant groups associated with the CSD. Bacteria and fungi on CS cooperatively formed a co-occurrence network to achieve the CSD. In the network, the key modules 2 and 4, mainly composed of phyla Proteobacteria and Ascomycota, directly promoted the CSD. Keystone taxa maintained the stability of microbial network structure and function, and regulated microbial groups associated with CSD in the key modules, rather than directly decomposing the CS. Notably, this study profoundly revealed that water temperature and total nitrogen (TN) regulated the keystone taxa and key modules in microbial interactions and then promoted the OMD. The two key modules 2 and 4 were significantly correlated with water temperature and TN in water, and two keystone taxa (bacterial genera Emticicia and Flavihumibacter) were significantly associated with TN. The research findings help us to understand the microbial mechanism of the OMD in rivers, which provides valuable insights into improving effective management strategies for river ecosystems.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".