Presence of <i>Legionella</i> spp. in cooling towers: the role of microbial diversity, <i>Pseudomonas</i> , and continuous chlorine application
Bibliographic record
Abstract
ABSTRACT Legionnaire’s Disease (LD) is a severe pneumonia caused by Legionella pneumophila . Cooling towers are the main source of L. pneumophila during large outbreaks. Colonization, survival, and proliferation of L. pneumophila in cooling towers are necessary for outbreaks to occur. These steps are affected by chemical and physical parameters of the cooling tower environment. We hypothesize that the bacterial community residing in the cooling tower could also affect the presence of L. pneumophila . A 16S rRNA targeted amplicon sequencing approach was used to study the bacterial community of cooling towers and its relationship with the Legionella spp. and L. pneumophila communities. The results indicated that the water source shaped the bacterial community of cooling towers. Several taxa were enriched and positively correlated with Legionella spp. and L. pneumophila . In contrast, Pseudomonas showed a strong negative correlation with Legionella spp. and several other genera. Most importantly, continuous chlorine application reduced microbial diversity and promoted the presence of Pseudomonas creating a non-permissive environment for Legionella spp . This suggests that disinfection strategies as well as the resident microbial population influences the ability of Legionella spp. to colonize cooling towers.
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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 | 0.001 | 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".