Why pathogens matter for meeting the united nations’ sustainable development goal 6 on safely managed water and sanitation
Bibliographic record
Abstract
Water and wastewater utilities, water and sanitation hygiene (WASH) practitioners, and regulating bodies, particularly in developing nations, rely heavily on indicator microorganisms, as opposed to pathogens, for much of their regulatory decisions. This commentary illustrates the importance of considering pathogens and not relying only on indicator organisms when making decisions regarding water and sanitation, especially with respect to meeting the current targets of the Sustainable Development Goal (SDG) 6. We use quantitative microbial risk assessment (QMRA) to present three common scenarios that WASH and public health practitioners encounter to illustrate our point. These include 1) chlorination of surface water for drinking, 2) land application of latrine waste as a fertilizer, and 3) recreation/domestic use of surface waters impacted by wastewater discharge. We show that the calculated probabilities of risk of infection are statistically significantly higher when using treatment/survival information for pathogens versus using indicator species data. Thus, demonstrating that relying solely on indicators for sanitation decision making is inadequate if we truly want to achieve the SDG6 targets of safely managed water and sanitation services.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".