Heterotrophic plate counts (HPC) in drinking water distribution systems: A comprehensive review and meta-analysis
Bibliographic record
Abstract
ABSTRACT Heterotrophic plate count (HPC) is widely assessed in drinking water distribution systems. However, methodological standards and guidelines on the use of HPC are not clearly defined. This comprehensive review and meta-analysis aim to evaluate HPC concentration and how they relate to the characteristics and operational conditions of systems. The size of the distribution system, use of chlorine or chloramine as secondary disinfection and the carbon content of the water were considered. Among 839 MEDLINE® records, 39 met our criteria and were included in the meta-analysis. Overall, wide ranges of HPC levels were observed in drinking water distribution systems. Results from the meta-analysis show a significant difference in concentrations between systems using chlorine or chloramine as secondary disinfectant and those that are not using any form of secondary disinfection. Similarly, results demonstrate a positive correlation between HPC levels and assimilable organic carbon. Assessing the spatial and temporal variations of HPC can provide useful information about the biological stability of the water and allow for routine analyses within individual drinking water systems. Due to its limitations as a global and unique indicator of water quality, HPC should be applied as part of a multi-parameter approach for microbial growth analysis in distribution networks.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".