Seasonal dynamics of the bacterioplankton community in a large, shallow, highly dynamic freshwater lake
Bibliographic record
Abstract
The spatiotemporal shifts of the bacterioplankton community can mirror their transition of functional traits in an aquatic ecosystem. However, the spatiotemporal variation of the bacterioplankton community composition structure (BCCS) within a large, shallow, highly dynamic freshwater lake is still poorly understood. Here, we examined the seasonal and spatial variability of the BCCs within Poyang Lake by sequencing the 16S rRNA gene amplicon to explore how hydrological changes affect the BCCs. Principal coordinate analysis showed that the BCCs varied significantly among four sampling seasons, but not spatially. The seasonal changes of the BCCs were mainly attributed to the differences between autumn and spring-winter. Higher α diversity indices were observed in autumn. Redundancy analysis indicated that the BCCs co-variated with water level, pH, temperature, total phosphorus, ammoniacal nitrogen, electrical conductivity, total nitrogen, and turbidity. Among them, water level was the key determinant separating autumn BCCs from the BCCs in other seasons. A significantly lower relative abundance of Burkholderiales (betI and betVII) and a higher relative abundance of Actinomycetales (acI, acTH1, and acTH2) were found in autumn than in other seasons. Overall, our results suggest that water level changes associated with pH, temperature, and nutrient status shaped the seasonal patterns of the BCCs within Poyang 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".