Contamination of public whirlpool spas: factors associated with the presence of<i>Legionella</i>spp.,<i>Pseudomonas aeruginosa</i>and<i>Escherichia coli</i>
Bibliographic record
Abstract
This work explores the factors associated with contamination of public spas by Legionella spp., Pseudomonas aeruginosa and Escherichia coli. Physicochemical and microbiological parameters were measured in water samples from 95 spas inQuébec, Canada. Spa maintenance was documented by a questionnaire. Legionella spp. were detected in 23% of spas, P. aeruginosa in 41% and E. coli in 2%. Bacteria were found in concerning concentrations (Legionella spp. ≥ 500 CFU/l, P. aeruginosa ≥ 51 CFU/100 ml or E. coli ≥ 1 CFU/100 ml) in 26% ofspas. Observed physicochemical parameters frequently differed from recommended guidelines. The following factors decreased the prevalence of concerning microbial contamination: a free chlorine concentration ≥ 2 mg/l or total bromine ≥ 3 mg/l (p = 0.001), an oxidation-reduction potential (ORP) > 650 mV (p = 0.001), emptying and cleaning the spa at least monthly (p = 0.019) and a turbidity ≤ 1 NTU (p = 0.013). Proper regulations and training of spa operators are critical for better maintenance of these increasingly popular facilities.
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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".