Deciphering microeukaryotic–bacterial co-occurrence networks in coastal aquaculture ponds
Why this work is in the frame
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Bibliographic record
Abstract
Microeukaryotes and bacteria are key drivers of primary productivity and nutrient cycling in aquaculture ecosystems. Although their diversity and composition have been widely investigated in aquaculture systems, the co-occurrence bipartite network between microeukaryotes and bacteria remains poorly understood. This study used the bipartite network analysis of high-throughput sequencing datasets to detect the co-occurrence relationships between microeukaryotes and bacteria in water and sediment from coastal aquaculture ponds. Chlorophyta and fungi were dominant phyla in the microeukaryotic-bacterial bipartite networks in water and sediment, respectively. Chlorophyta also had overrepresented links with bacteria in water. Most microeukaryotes and bacteria were classified as generalists, and tended to have symmetric positive and negative links with bacteria in both water and sediment. However, some microeukaryotes with high density of links showed asymmetric links with bacteria in water. Modularity detection in the bipartite network indicated that four microeukaryotes and twelve uncultured bacteria might be potential keystone taxa among the module connections. Moreover, the microeukaryotic-bacterial bipartite network in sediment harbored significantly more nestedness than that in water. The loss of microeukaryotes and generalists will more likely lead to the collapse of positive co-occurrence relationships between microeukaryotes and bacteria in both water and sediment. This study unveils the topology, dominant taxa, keystone species, and robustness in the microeukaryotic-bacterial bipartite networks in coastal aquaculture ecosystems. These species herein can be applied for further management of ecological services, and such knowledge may also be very useful for the regulation of other eutrophic ecosystems. Supplementary Information: The online version contains supplementary material available at 10.1007/s42995-022-00159-6.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it