Prognosticating the Impact of Seasonal Variability on Microbial Load in Water Bodies Around Dhaka City, Bangladesh
Bibliographic record
Abstract
Background and Objective: Surface water of different reservoirs in Bangladesh is often contaminated with biological, chemical and physical wastes.Increasing population, urbanization and industrialization are contributing to the situation.The study aimed to assess seasonal variations in microbial load in water bodies surrounding Dhaka City, Bangladesh.Materials and Methods: Two rivers (Turag and Burigonga) and lakes (Banani and Dhanmondi) in and around Dhaka City were selected for the current study.Spread plate techniques were conducted to obtain bacterial load in water samples.Total Viable Count (TVC), Total Coliform Count (TCC), Total Fecal Coliform Count (TFCC) and Total Salmonella Shigella Count (TSSC) were conducted as well and data were represented as CFU/mL in a log scale.Results: The majority of the water reservoir exhibited a declining bacterial load during winter whereas it is significantly high during spring.Major bacterial genera in the water bodies included in this study were Staphylococcus aureus, Micrococcus sp., E. coli, Salmonella sp., Shigella sp., Enterobacter sp., Pseudomonas sp., Acinetobacter sp., Proteus sp. and both river water and lake water harbored almost similar genera of bacteria.In general, there is a declining trend of microbial load (TVC CFU/mL; TCC CFU/mL; TFC CFU/mL and TSSC CFU/mL) in both types of water bodies around Dhaka City, Bangladesh.Conclusion: The current study implies that pathogenic isolates such as Escherichia coli, Salmonella sp., Enterobacter S. aureus, Klebsiella sp., Citrobacter sp., Shigella sp., Pseudomonas sp. can cause serious health issues if unsafe water from these reservoirs is used for day to day activity without treatment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".