Comparison of Chemical, Biological and Physical Quality Assessment of Indoor Swimming Pools in Shahrekord City, Iran in 2013
Bibliographic record
Abstract
Previous studies have shown that mismanaged swimming pools could transmit water-borne diseases. The objective of the present study was the quality assessment of chemical, biological and physical characteristics of swimming pools in Shahrekord city, southwest of Iran. The two main indoor swimming pools of Shahrekord city were considered during the summer and winter of 2013. The number of 459 samples were analysed from swimming pools, showers and dressing rooms for chemical, biological and physical quality assessment. The most prevalent fungi were Aspergillus (48.91%), Penicillium (22.9%), Nocardia (11.31%), Cladosporium (8.41%). Rhizopus (6.18%), Scopulariopsis (6.21%), Fusarium (5.31%), and Mucor (1.38%). The most fungal contamination sites for both swimming pools were showers. Results showed that the values of total faecal coliform, Pseudomonas aeruginosa, Legionalla, Escherichia coli and Heterotrophic Plate Count (HPC) for both swimming pools exceeded the guidelines, except for Staphylococcus aureus. The correlation coefficient between bathers load and total faecal coliform, heterotrophic bacteria was 0.949. The turbidity, free residual chlorine, and hardness of both swimming pools were not compliance with standard guidelines. Therefore, the improvement of disinfection and cleaning procedures is necessary, due to the different users and daily bather loads of each pool, as well as monitoring the water quality and increasing of the knowledge of swimming pool users on the risks of these potential diseases.
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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 | 0.001 | 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 | 0.001 | 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".