Genetic diversity of picoeukaryotes in eight lakes differing in trophic status
Bibliographic record
Abstract
The genetic diversity of picoeukaryotes (0.2-5.0 µm) was investigated in 8 lakes differing in trophic status in Nanjing, China. Denaturing gradient gel electrophoresis (DGGE) and cloning and sequencing of 18S rRNA genes were applied to analyze the picoeukaryotic communities. DGGE analysis showed that among the 8 lakes, the diversity of picoeukaryotes was greatest in the mesotrophic Lake Nan (24 bands) and least in the oligotrophic Lake Qian (12 bands). Cluster analysis of DGGE profiles revealed that the 8 lakes were grouped into 2 distinct clusters. Cluster 1 contained lakes Mochou, Zixia, Huashen, Nan, Pipa, and Qian, while cluster 2 contained lakes Xuanwu and Baijia. Clone libraries were constructed from the mesotrophic Lake Xuanwu and the oligotrophic Lake Zixia, and the 2 libraries were compared using the program LIBSHUFF. This analysis indicated that the picoeukaryotic community composition differed significantly between the 2 lakes (p = 0.001). A total of 25 operational taxonomic units were detected; 18 (62 clones) were related to known eukaryotic groups, while 7 (30 clones) were not affiliated with any known eukaryotic group. Alveolates and stramenopiles were the dominant groups in Lake Xuanwu, while alveolates and chlorophyta predominated in Lake Zixia. Multivariate statistical analysis indicated that the differences in the picoeukaryotic community composition of the 8 lakes might be related to trophic status and top-down regulation by metazooplankton.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".