Phylogenetic assessment of heterotrophic bacteria from a water distribution system using 16S rDNA sequencing
Bibliographic record
Abstract
Determination of a heterotrophic plate count (HPC) for drinking-water samples alone is not enough to assess possible health hazards associated with sudden changes in the bacterial count. Speciation is very crucial to determine whether the population includes pathogens and (or) opportunistic pathogens. Most of the isolates recovered from drinking water samples could not be allocated to a specific phylogenetic branch based on the use of conventional diagnostic methods. The present study had to use phylogenetic analysis, which was simplified by determining and using the first 500-bp sequence of the 16S rDNA, to successfully identify the type and species of bacteria found in the samples. Gram-positive bacteria alpha-, beta-, and gamma-Proteobacteria were found to be the major groups representing the heterotrophic bacteria in drinking water. The study also revealed that the presence of sphingomonads in drinking water supplies may be much more common than has been reported so far and thus further studies are merited. The intermittent mode of supply, mainly characterized by water stagnation and flow interruption associated possibly with biofilm detachment, raised the possibility that the studied bacterial populations in such systems represented organisms coming from 2 different niches, the biofilm and the water column.
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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 | 0.001 |
| 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 | 0.000 | 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".