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Effect of ozone nanobubbles on the microbial ecology of pond water and safety for jade perch (Scortum barcoo)

2023· article· en· W4383215902 on OpenAlexfundno aff
Qianjun Huang, Pok Him Ng, Ana Rita Marques, Tzu Hsuan Cheng, Ka Yan Man, Kwok Zu Lim, Brett MacKinnon, Liqing Huang, Ju Zhang, Ladan Jahangiri, William Eduardo Furtado, F. M. Yasir Hasib, Liang Zhong, Ho Yat Kam, Chun Ting Lam, Hong Liu, Ying Yang, Wenlong Cai, Dominic L.D.F. Brettell, Sophie St‐Hilaire

Bibliographic record

VenueAquaculture · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsBiologyEcologyMicrobial population biologyAquacultureEcosystemOzoneMicrobial ecologyAquatic ecosystemMicroorganismEnvironmental chemistryBacteriaFisheryFish <Actinopterygii>Chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.259
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2023
Admission routes1
Has abstractno

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