DNA Barcoding and Water Quality Analysis of Nitrifying Bacteria in Lebak Lebung Swamp, South Sumatera
Why this work is in the frame
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Bibliographic record
Abstract
Aquaculture development activities in swamp water has the problem of contamination from organic matter, and this waste has the potential for environmental challange.Nitrifying bacteria are a natural instrument that can play a role in maintaining the stability of the quality of swamp waters through their role as bioremediator.Therefore, its presence is important to identify in waters.The aim of the research is to determine the types and characteristics of bioremediation bacteria, construct a phylogenetic tree and the relationship between water quality and the bioremediation process by bacteria so that in the future it can be applied to waters that have the same problems or become a bioindicator for certain pollutants, especially in the area of Lebak Lebung Swamp, Ogan.Ilir, South Sumatra.The method used is taking bacterial samples, isolating bacteria using Nutrient Agar (NA) media, observing bacterial morphology, DNA sequencing, amplifying DNA mitochondria COI using PCR (Polymerase Chain Reaction).The results of BLASTn (Basic Local Alignment Search Tool-nucleotide) analysis showed the highest percentage of identity, namely 93%, with the Burkholderia cepacia strain NBRAJG97 from India and Burkholderia sp.strain 172 1492R comes from Estonia which indicates that the bacteria found belong to the Burkholderia bacteria group.Water quality measurement was temperature 34.7-35.4℃,dissolved oxygen 6.0-6.2 mgL -1 , pH 6.2, ammonia 0.05 mgL -1 .Based on air quality indications such as low ammonia content, this could indicate that the Burkholderia bacteria found in Lebak Lebung Swamp play a role as bioremediation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it