Investigation of Bacteria and Fish Pathogenic Bacteria Found in Freshwater Aquaponic System
Bibliographic record
Abstract
Bacteria are very vital organisms in aquaponic system decomposing and converting the toxic components of the fish waste into usable vegetable nutrients; however, some unwanted bacteria could cause fish diseases. The purposes of this research were to compare the amount of bacteria in water from aquaponic catfish cultured in different stocking densities and Denaturing Gradient Gel Electrophoresis (DGGE) of Polymerase Chain Reaction (PCR)-amplified 16S rDNA was applied to characterise the bacterial populations. Three different stocking densities; 80, 100 and 120 fish/m2 of hybrid catfish were cultured in aquaponic system. Each treatment contained 3 replications; Treatment 1, 360 catfish (80 fish/m2, without lettuce); Treatment 2, 360 catfish (80 fish/m2, without lettuce); Treatment 3, 450 catfish (100 fish/m2); and treatment 4, 540 catfish (120 fish/m2). This research was conducted for 4 months. Water was sampled two times before fish stocking, it was found that the number of bacteria in the first week were higher than the third week. The average number of bacteria in water from treatment 1, 2, 3 and 4 were (2.49 ± 0.40) ×107, (2.43 ± 0.23) ×107, (2.48 ± 2.22) ×107 and (2.29 ± 2.38) ×107 Colonies forming unit (CFU)/ml respectively. According to four-time samplings after catfish were stocked, the average number of bacteria in water from treatment 1, 2, 3 and 4 were (1.85 ± 0.53) ×107, (2.73 ± 0.15) ×107, (2.45 ± 0.30) ×107 and (2.24 ± 0.19) ×107 CFU/ml respectively. The average number of bacteria in water after lettuce harvest from treatment 1, 2, 3 and 4 were (0.185 ± 0.04) ×107, (0.26 ± 0.04) ×107, (0.30 ± 0.01) ×107 and (0.29 ± 0.05) ×107 CFU/ml respectively. There were no differences in bacterial counts among treatments (p > 0.05). In addition, no significant difference was observed in the number of bacteria in filter tanks (p > 0.05) and the number were quite similar to the ones in catfish tanks. Aeromonas hydrophila and Flavobacterium columnare infected catfish at the beginning of the experiment; however, it got better and no more death found after acclimation. The DGGE analysis of PCR-amplified 16S rDNA fragments revealed that bacterial populations in water were dominated by Aeromonas spp., Pseudomonas spp. and Staphylococcus spp. Antibiotic sensitivity test indicated that enrofloxacin might be the best option for bacterial infection treatment. The results indicate that aquaponic system could reduce the wastewater from catfish culture; however, maintaining a good water quality environment for fish culture should take into account.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".