Waterborne Pathogens: A Critical Assessment of Methods, Results and Data Analysis
Bibliographic record
Abstract
Humans harbour a variety of pathogens that are often transmitted from other animal species. A few are localized in tropical areas, but most enteric pathogens are present everywhere on the planet and they travel with their hosts to distant locations. Several of these microorganisms are transmitted by water that has been contaminated by fecal matter, whereas some are normally found in water but, given an opportunity, will cause disease (i.e., opportunistic pathogens). While waterborne outbreaks have been reported for many pathogens, assessing the proportion of the disease burden to a specific route and pathogen has proven quite elusive. Surveillance of disease in populations, even when actively done, is very inaccurate as it often gathers data on the most acute cases that are only a very small proportion of the true number of infected individuals. There are several issues discussed herein, focusing on the needs and gaps linked to waterborne pathogen monitoring. The benefits and weaknesses of current and emerging methodologies are discussed, in addition to the appropriateness of allocating resources to waterborne pathogen monitoring. The most critical gap is the lack of validation of most methods used in environmental microbiology for the detection of pathogens. Data generated by various laboratories are currently extremely difficult to compare and cannot serve as the basis for risk assessment or management. The issue of laboratory capacity is also raised, within the context of the availability of trained personnel, the application of QA/QC protocols, and accreditation on a national level. In closing, needs are identified for informed communication of the risks of waterborne pathogens, the training of highly qualified personnel, and the development and standardization of methods that will ultimately enhance water safety and public health protection.
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 | 0.382 | 0.546 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".