Current and emerging microbiology issues of potable water in developed countries
Bibliographic record
Abstract
Water is vital for life, for commercial and industrial purposes and for leisure activities in the daily lives of the world's population. Diarrhoeal disease associated with consumption of poor quality water is one of the leading causes of morbidity and mortality in developing countries (especially in children <5 years old). In developed countries, whilst potable water is not a leading cause of death, it still can still pose a significant health risk. Water quality is assessed using a number of criteria, e.g., microbial load and nutrient content which affects microbial survival, as well as aesthetic factors such as odor. In water systems, the presence of disinfectant, low temperatures, flow regimes and low organic carbon sources do not appear to be conducive to microbial persistence. However, frequently this is not the case. A variety of human pathogens can be transmitted orally by water and in the developed world water quality regulations require that potable water contains no microbial pathogens. Chlorine dioxide is a safe, relatively effective biocide that has been widely used for drinking water disinfection for 40 years. Providing the water is of low turbidity, standard chlorination procedures are sufficient to prevent the spread of planktonic bacteria along water mains. However, despite this, bacterial contamination of water distribution systems is well documented, with growth typically occuring on surfaces, including pipe walls and sediments. Rivers, streams and lakes are all important sources of drinking water and are used routinely for recreational purposes. However, due to fouling by farm and wild animals, these sources can be contaminated with microbes, e.g., chlorine-resistant Cryptosporidium oocysts, no matter how pristine the source or well maintained the water delivery system. The high incidence of Cryptosporidium in surface water sources underlines the need for frequent monitoring of the parasite in drinking water. The use of coliforms as indicator organisms, although considered relevant to most cases, is not without limitations, and is thus not a completely reliable parameter of water safety, e.g., Campylobacter contamination cannot be accurately predicted by coliform enumeration. Furthermore, the presence of biofilms and bacterial interactions with protozoa in water facilitate increased resistance to antimicrobial agents and procedures such as disinfectants and heating, e.g., Legionnaires' disease caused by Legionella pneumophila. The high cost of waterborne disease outbreaks should be considered in decisions regarding water utility improvement and treatment plant construction. The control of human illnesses associated with water would be aided by a greater understanding of the interactions between water-borne protozoa and bacterial pathogens, which until relatively recently have been overlooked.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".