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Record W4294597554 · doi:10.47853/fas.2022.e39

Effect of nanobubbles (oxygen, ozone) on the Pacific white shrimp (Penaeus vannamei), Vibrio parahaemolyticus and water quality under lab conditions

2022· article· en· W4294597554 on OpenAlexfundno aff
Nguyễn Hữu Nghĩa, Nguyen Thi Nguyen, Phan Trọng Bình, Le Thi May, Tong Tran Huy, Phạm Thái Giang, Sophie St‐Hilaire, Phan Thi Van

Bibliographic record

VenueFisheries and aquatic sciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsnot available
FundersDepartment of Health and Social CareInternational Development Research CentreGovernment of the United Kingdom
KeywordsVibrio parahaemolyticusShrimpPenaeusLitopenaeusOzoneWater qualityFisheryBiologyVibrioFood scienceMicrobiologyChemistryBacteriaEcology

Abstract

fetched live from OpenAlex

This study assessed the effects of oxygen and ozone nanobubbles on gill morphology, weight gain, and mortality of Pacific white shrimp (Penaeus vannamei), as well as the level of Vibrio parahaemolyticus and water quality of shrimp culture tanks under lab conditions. Two experiments were carried out with oxygen macrobubble, ozone macrobubble, oxygen nanobubble, ozone nanobubble, and control treatments (air-stone macrobubble). Experiments were done in triplicate in 100 L tanks with 15‰ saline water, and 20 shrimp per tank. Tanks in Experiment 1 were not inoculated with bacteria; tanks in Experiment 2 were inoculated with V. parahaemolyticus at a concentration of 106 CFU/mL. The results revealed that short treatments with ozone nanobubbles had minimal impact on shrimp gills, mortality, and growth rates, reduced V. parahaemolyticus concentration in water compared to the other groups, and improve water quality. These laboratory results indicate that ozone nanobubble treatment may be useful for controlling V. parahaemolyticus. More work is needed to find the best protocol to apply the technology on a commercial scale.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.276
Teacher spread0.252 · 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 teacher head, not a consensus.

Study designObservational
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

Citations19
Published2022
Admission routes1
Has abstractyes

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