Fish growth enhances microbial sulfur cycling in aquaculture pond sediments
Bibliographic record
Abstract
Microbial sulfate reduction and sulfur oxidation are vital processes to enhance organic matter degradation in sediments. However, the diversity and composition of sulfate-reducing bacteria (SRB) and sulfur-oxidizing bacteria (SOB) and their environmental driving factors are still poorly understood in aquaculture ponds, which received mounting of organic matter. In this study, bacterial communities, SRB and SOB from sediments of aquaculture ponds with different sizes of grass carp (Ctenopharyngodon idellus) were analysed using high-throughput sequencing and quantitative real-time PCR (qPCR). The results indicated that microbial communities in aquaculture pond sediments of large juvenile fish showed the highest richness and abundance of SRB and SOB, potentially further enhancing microbial sulfur cycling. Specifically, SRB were dominated by Desulfobulbus and Desulfovibrio, whereas SOB were dominated by Dechloromonas and Leptothrix. Although large juvenile fish ponds had relatively lower concentrations of sulfur compounds (i.e. total sulfur, acid-volatile sulfide and elemental sulfur) than those of larval fish ponds, more abundant SRB and SOB were found in the large juvenile fish ponds. Further redundancy analysis (RDA) and linear regression indicated that sulfur compounds and sediment suspension are the major environmental factors shaping the abundance and community structure of SRB and SOB in aquaculture pond sediments. Findings of this study expand our current understanding of microbial driving sulfur cycling in aquaculture ecosystems and also provide novel insights for ecological and green aquaculture managements.
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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) | 0.001 | 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) | 0.002 | 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".