Factors Affecting the Dynamics of <i>Legionella pneumophila</i>, Nontuberculous Mycobacteria, and Their Host <i>Vermamoeba vermiformis</i> in Premise Plumbing
Bibliographic record
Abstract
Premise plumbing are complex systems with diverse materials and variable temperature and stagnation time. Opportunistic pathogens are commonly found in premise plumbing, but little is known about the impact of design and operational characteristics of building water systems on their occurrence and survival in water and biofilms. The main objective of this study was to investigate the interplay between material type, temperature, and stagnation on the occurrence and survival of Legionella pneumophila, nontuberculous mycobacteria (NTM), and their host Vermamoeba vermiformis . Twelve CDC biofilm reactors were used to compare concentrations of these microorganisms measured by qPCR in water and biofilms, at 25, 40, 55, or 60 °C, and in contact with six materials: polypropylene (PP), polyvinyl chloride (PVC), EPDM, cross-linked polyethylene (PEX), stainless steel (SS), and copper (Cu). The addition of V. vermiformis induced an average 1.4-log increase in NTM concentrations, while the spiking of two L. pneumophila strains caused a 3-log decrease in NTM in PP and PEX biofilms. Heating at 55 and 60 °C in copper reactors led to the decline in V. vermiformis and L. pneumophila below the qPCR quantification limit. A rebound of culturable L. pneumophila was noted after a 6 month stagnation period at room temperature in water with PVC and stainless steel coupons.
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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 | 0.000 | 0.000 |
| 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".