Global microbial water quality data and predictive analytics: Key to health and meeting SDG 6
Bibliographic record
Abstract
Microbial water quality is an integral to water security and is directly linked to human health, food safety, and ecosystem services. However, specifically pathogen data and even faecal indicator data (e.g., E . coli ), are sparse and scattered, and their availability in different water bodies (e.g., groundwater) and in different socio-economic contexts (e.g., low- and middle-income countries) are inequitable. There is an urgent need to assess and collate microbial data across the world to evaluate the global state of ambient water quality, water treatment, and health risk, as time is running out to meet Sustainable Development Goal (SDG) 6 by 2030. The overall goal of this paper is to illustrate the need and advocate for building a robust and useful microbial water quality database and consortium worldwide that will help achieve SDG 6. We summarize available data and existing databases on microbial water quality, discuss methods for producing new data on microbial water quality, and identify models and analytical tools that utilize microbial data to support decision making. This review identified global datasets (7 databases), and regional datasets for Africa (3 databases), Australia/New Zealand (6 databases), Asia (3 databases), Europe (7 databases), North America (12 databases) and South America (1 database). Data are missing for low- and middle-income countries. Increased laboratory capacity (due to COVID-19 pandemic) and molecular tools can identify potential pollution sources and monitor directly for pathogens. Models and analytical tools can support microbial water quality assessment by making geospatial and temporal inferences where data are lacking. A genomics, information technology (IT), and data revolution is upon us and presents unprecedented opportunities to develop software and devices for real-time logging, automated analysis, standardization, and modelling of microbial data to strengthen knowledge of global water quality. These opportunities should be leveraged for achieving SDG 6 around the world.
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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 | 0.023 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".