Responses of bacterial and three sub-microeukaryote communities in the water of white shrimp <i>Penaeus vannamei</i> aquaculture ponds in two polyculture models
Bibliographic record
Abstract
Polyculture operations in freshwater aquaculture ponds can disrupt microbial communities. High-throughput sequencing was used to assess the impact of polyculture operations on bacterial and three sub-microeukaryote communities (fungi, zooplankton, and eukaryotic phytoplankton) in Penaeus vannamei aquaculture ponds containing oriental river prawns and giant freshwater prawns, respectively. The results showed that the bacterial community was less sensitive than the microeukaryote communities to both the polyculture activity and environmental variations. The polyculture of giant freshwater prawns rather than oriental river prawns was the primary factor affecting the beta diversity of the three sub-microeukaryote communities. This may be due to the larger biomass of the polyculture varieties of giant freshwater prawns compared with oriental river prawns. The polyculture activity of giant freshwater prawns with a higher density and that of oriental river prawns with a lower density increased the stochasticity of the community assembly of the three sub-microeukaryote communities. It also affected the topological properties of the microbial communities, including greater correlations between ecosystem elements, and reducing the correlations among zooplanktons. The eukaryotic phytoplankton was the only microbial community that could also be explained by nutrient variation (mainly the total nitrogen). This highlights the potential role of the eukaryotic phytoplankton as a suitable indicator of the effects of nutrient input into 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".