Monitoring of Heterotrophic Plate Count (HPC) in Treated Water from Distribution System
Bibliographic record
Abstract
Heterotrophic plate count (HPC) is a method that enumerates the total number of viable heterotrophs whichinclude bacteria, yeasts and molds. In water quality monitoring, it is performed to measure changes in the microbiological diversity in water treatment and distribution systems, as well as recreational waters and drinking water. It can also indicate risk of other pathogens’ regrowth in water and biofilm formation. Althoughthe Japanese water quality guideline recommends limit of HPC <100 CFU/mL and Canadian & US guidelines of<500 CFU/mL,the reporting of this parameter has not been developed by the Ministry of Health, Malaysia for treated water. Therefore,this study aimed at establishing a monitoring programme for the enumeration of HPC in treated water from distribution system using the ready-to-useCompact Dry AQ media via membrane filtration method. Out of 178 selected treated water samples, only 3 samples showed HPC positive and their counts were between 6 - 21 CFU/100mL, which were below the recommended limits. Hence, it can be suggested that the water treatment and disinfection performance at water treatment processes were adequate during the monitoring period, which further ensures clean and safe water supply to consumers.Further works that can be proposedinclude resampling and retesting of targeted repeating positive HPC sampling points. Meanwhile, other sampling points from different locations may also be included in future monitoring programme.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".