Dissolved organic carbon and relationship with bacterioplankton community composition in 3 lake regions of Lake Taihu, China
Bibliographic record
Abstract
To clarify the relationships between dissolved organic carbon (DOC) and bacterioplankton community composition (BCC), a 1-year survey (June 2009 - May 2010) was conducted in 3 regions of Lake Taihu (Meiliang Bay, Lake Center, and Eastern Taihu), China. Polymerase chain reaction - denaturing gradient gel electrophoresis was used to analyze the composition and heterogeneity of the bacterioplankton community. Canonical correspondence analysis was used to explore the relationships between DOC concentration and BCC. We found a significant negative correlation between DOC concentration and bacterioplankton community diversity (as measured by the Shannon-Wiener index (H')). The results show that spatial variation in the bacterioplankton population was stronger than the seasonal variation and that DOC concentration influences BCC in Lake Taihu. DOC concentration, followed by macrophyte biomass, water turbidity, and phytoplankton biomass were the most influential factors that account for BCC changes in Lake Taihu. More detailed studies on the relationship between DOC concentration and BCC should focus on differences in DOC concentrations and quality among these lake regions. DOC had a significant impact on BCC in Meiliang Bay. The relationship between DOC and BCC in the 2 other regions studied (Lake Center and Eastern Taihu) was weaker. The results of this study add to our understanding of the BCC in eutrophic lakes, especially regarding the role of the microbial loop in lake 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 | 0.002 |
| Science and technology studies | 0.001 | 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".