Qualitative microbiome profiling along a wastewater system in Kampala, Uganda
Bibliographic record
Abstract
Kampala, the capital city of Uganda, is rapidly expanding without adequate wastewater treatment facilities to accommodate the current estimated population of 1.68 million people. Hence, freshwater bodies and natural ecosystems around the city are heavily polluted with organic and inorganic contaminants. Yet, there is a paucity of data on pathogenic microorganisms, which potentially threatens health of local communities. We performed a qualitative microbial analysis using a whole metagenome sequencing approach encompassing over 150 gigabases of sequencing data to characterize the Nakivubo wastewater system, which includes a wastewater channel and surrounding wetlands. We found that microbial diversity is heterogeneous throughout the system and that three community state types could be differentiated. We showed the presence of various waterborne agents of gastrointestinal infections in humans, which were associated with leakage occurring around two locations along the wastewater channel. Our data indicate that the microbial decontamination capacity of the local wastewater treatment facility was insufficient at the time of sampling, and that several areas of the wetlands were contaminated with human pathogens, indicating that parts of the wetlands are potentially unsafe for urban agriculture.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".