Effects of benthic macrofauna bioturbation on the bacterial community composition in lake sediments
Bibliographic record
Abstract
Benthic macrofauna are considered to be an important part of the lacustrine ecosystem, and bioturbation may greatly affect the biogeochemical processes and microbial activities in sediments. In the present study, the bacterial community composition in sediments inhabited by 3 different types of benthic macrofauna (Corbicula fluminea, Chironomidae larvae, and tubificid worms) in the shallow and eutrophic Lake Taihu was studied to investigate the different effects of bioturbation on the composition of these communities. Microcosms were constructed, and culture-independent methods, including terminal restriction fragment length polymorphism (T-RFLP) and clone library analysis, were performed to evaluate the bacterial communities. Analysis of similarities (ANOSIM) and multidimensional scaling (MDS) analysis of T-RFLP patterns demonstrated that differences in the bacterial community composition between the control and the macrofauna-inhabited sediments were not as great as expected, although the chemical properties of the sediments changed remarkably. Nevertheless, the dominant bacterial group in each type of macrofauna-inhabited sediment was different. Acidobacteria, Betaproteobacteria, and Deltaproteobacteria were the dominant bacterial groups in sediments inhabited by C. fluminea, tubificid worms, and Chironomidae larvae, respectively. The data obtained in this study are helpful for understanding the effects of bioturbation in a shallow, eutrophic lake.
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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 | 0.001 |
| 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".