Effect of ozone nanobubbles on the microbial ecology of pond water and safety for jade perch (Scortum barcoo)
Bibliographic record
Abstract
The microbial composition and diversity in aquaculture pond ecosystems are closely related to animal productivity and disease outbreaks. Interventions that alter the bacteria found in pond water can therefore affect the productivity of these systems. Ozone nanobubbles have recently been shown to reduce pathogens, improve dissolved oxygen, and influence fish innate immunity. However, little is known about the effect of nanobubble treatment on the microbial community of aquaculture ponds. This study investigated the impact of ozone macrobubbles (O3MB) and nanobubbles (O3NB) on the microbial ecology of pond water and fish health. We successfully eliminated between 90.9 and 99.4% of the heterotrophic bacteria and 90.1 to 95.2% of the bacterial DNA in our small pond water ecosystems after treatment with 0.15 mg/L ozone. According to the shotgun metagenomic sequencing, ozone macro- and nanobbuble treatments reduced the relative abundance of all bacteria in our water sample, including the dominant bacterial species, as well as Cyanobacteria. The top ten bacterial species in the community changed and were more evenly distributed within the water sample. The bacterial richness of the ozone-treated water samples declined slightly, but over 6000 species were still identified 24 h after the treatment. We also observed a rebound in the bacterial community 24 h after the ozone treatments. The advantage of the nanobubble delivery of ozone over macrobubble delivery of this gas was that the former took significantly less time to deliver the desired quantity of gas while it also greatly increased the dissolved oxygen in the water. Further, we assessed the impact of ozone nanobubbles on jade perch, and no effects were found on the fish at an exposure dose of 0.15 mg/L. This study provides preliminary information on potential applications of nanobubble technology for “resetting” microbial communities, which may be useful during disease outbreaks.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".