Abundant and Rare Bacterioplankton in Freshwater Lakes Subjected to Different Levels of Tourism Disturbances
Bibliographic record
Abstract
Anthropogenic disturbances have a negative impact on lake ecosystems, such as water environmental degradation. Bacterioplankton communities are essential components in lakes and consist of a few abundant species and several rare taxa. However, little is known about the community diversity and composition of abundant and rare bacterioplankton subjected to different levels of anthropogenic disturbances. In this study, water samples were collected from twelve freshwater lakes located around the city of Nanjing, China. Both Illumina MiSeq sequencing and multivariate statistical analysis were employed to determine the bacterioplankton community composition and its relation to environmental variables. The results indicated that tourism disturbances (mostly sewage discharge and tourist activities) altered the community structure of both abundant and rare bacterioplankton by changing water physicochemical characteristics. Alpha diversity of both abundant and rare taxa did not differ among different anthropogenic disturbance lakes (p > 0.05). Rare bacterial taxa possessed higher alpha diversity than abundant taxa, though rare taxa occupied a tiny portion of abundance (4.5%). Redundancy analysis demonstrated that dissolved organic carbon (DOC) was the most significant correlation variable for constraining the variation of abundant taxa, whereas total phosphorus (TP), ammonium nitrogen (NH4+-N), and chlorophyll-a (Chl-a) were the most dominant environmental factors constraining the rare taxa, indicating abundant and rare taxa may have different ecological niches.
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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".