Comparing environmental microbiota of different growth stages of fresh water turtle Chinemys reevesii in pond and greenhouse cultivation
Bibliographic record
Abstract
<title>Abstract</title> The microbial community structure is an important indicator for evaluating the water quality of the aquaculture environment. In this study, V4 regions of 16S rRNA gene of pond (PC) and greenhouse cultured (GC)<italic> C. reevesii</italic> were sequenced. Results showed that a total of 1,993,090 high quality counts and 105,159 observed OUTs were obtained; and the Chao1 richness estimator of PC was significantly higher than that of GC groups. Beta-diversity showed that the microbiota of two groups were isolated from each other. In addition, the correlation analysis of environmental factors showed that NO2-N, PH, PO4-P, and stocking density played significant roles in the bacterial community composition. The dominant phyla in PC groups were cyanobacteria, proteobacteria, actinobacteria, bacteroidetes, verrucomicrobia, planctomycetes; and in GC groups were proteobacteria, bacteroidetes, firmicutes, cyanobacteria, chloroflexi, actinobacteria. The functional prediction showed that the top5 Picrust prediction gene functions were protein processing in endoplasmic reticulum, retinol metabolism, proteasome, glycan binding proteins, and stilbenoid, diarylheptanoid and gingerol biosynthesis. Meanwhile, the numbers and types of KEGG pathway annotations showed a significant difference between the two cultivation environments. The prediction of bacterial phenotype implied that the GC environment is more likely to deteriorate, and turtles are more susceptible to pathogens than those of PC environment. This is the first report to explore and understand the difference of microbiota characteristics between different cultivation environments in different growth stages of <italic>C. reevesii</italic>, which will provide basic data for water quality adjustment, disease prevention, and healthy breeding of turtle.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 teacher head, 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".