Environmental (Saprozoic) Pathogens of Engineered Water Systems: Understanding Their Ecology for Risk Assessment and Management
Bibliographic record
Abstract
Major waterborne (enteric) pathogens are relatively well understood and treatment controls are effective when well managed. However, water-based, saprozoic pathogens that grow within engineered water systems (primarily within biofilms/sediments) cannot be controlled by water treatment alone prior to entry into water distribution and other engineered water systems. Growth within biofilms or as in the case of Legionella pneumophila, primarily within free-living protozoa feeding on biofilms, results from competitive advantage. Meaning, to understand how to manage water-based pathogen diseases (a sub-set of saprozoses) we need to understand the microbial ecology of biofilms; with key factors including biofilm bacterial diversity that influence amoebae hosts and members antagonistic to water-based pathogens, along with impacts from biofilm substratum, water temperature, flow conditions and disinfectant residual-all control variables. Major saprozoic pathogens covering viruses, bacteria, fungi and free-living protozoa are listed, yet today most of the recognized health burden from drinking waters is driven by legionellae, non-tuberculous mycobacteria (NTM) and, to a lesser extent, Pseudomonas aeruginosa. In developing best management practices for engineered water systems based on hazard analysis critical control point (HACCP) or water safety plan (WSP) approaches, multi-factor control strategies, based on quantitative microbial risk assessments need to be developed, to reduce disease from largely opportunistic, water-based pathogens.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".