<i>Pseudomonas aeruginosa</i> in premise plumbing of large buildings
Bibliographic record
Abstract
Pseudomonas aeruginosa is an opportunistic bacterial pathogen that is widely occurring in the environment and is recognized for its capacity to form or join biofilms. The present review consolidates current knowledge on P. aeruginosa ecology and its implication in healthcare facilities premise plumbing. The adaptability of P. aeruginosa and its capacity to integrate the biofilm from the faucet and the drain highlight the role premise plumbing devices can play in promoting growth and persistence. A meta-analysis of P. aeruginosa prevalence in faucets (manual and electronic) and drains reveals the large variation in device positivity reported and suggest the high variability in the sampling approach and context as the main reason for this variation. The effects of the operating conditions that prevail within water distribution systems (disinfection, temperature, and hydraulic regime) on the persistence of P. aeruginosa are summarized. As a result from the review, recommendations for proactive control measures of water contamination by P. aeruginosa are presented. A better understanding of the ecology of P. aeruginosa and key influencing factors in premise plumbing are essential to identify culprit areas and implement effective control measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".