DNA Barcoding and Water Quality Analysis of Nitrifying Bacteria in Lebak Lebung Swamp, South Sumatera
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.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".