Submerged macrophytes modify bacterial community composition in sediments in a large, shallow, freshwater lake
Bibliographic record
Abstract
Submerged aquatic macrophytes are an important part of the lacustrine ecosystem. In this study, the bacterial community compositions in the rhizosphere sediments from three kinds of submerged macrophytes (Ceratophyllum demersum, Potamogeton crispus, and Vallisneria natans) were investigated to determine whether submerged macrophytes could drive the variation of bacterial community in the eutrophic Taihu Lake, China. Molecular techniques, including terminal restriction fragment length polymorphism (T-RFLP) of PCR-amplified 16S rRNA gene and clone libraries, were employed to analyze the bacterial community compositions. Remarkable differences of the T-RFLP patterns were observed among the different samples, and the results of LIBSHUFF analysis also confirmed that the bacterial community compositions in the rhizosphere sediments of three kinds of submerged macrophytes were statistically different from that of the unvegetated sediment. Acidobacteria, Deltaproteobacteria, and Betaproteobacteria were the dominant bacterial groups in the rhizosphere sediments of Ceratophyllum demersum, Potamogeton crispus, and Vallisneria natans, respectively, accounting for 15.38%, 29.03%, and 18.00% of the total bacterial abundances. Our study demonstrated that submerged macrophytes could influence the bacterial community compositions in their rhizosphere sediments, suggesting that macrophytes have an effect on the cycling and transportation of nutrients in the freshwater lake 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".