Accuracy of swimming pool test kits
Bibliographic record
Abstract
Background: Pool Chemistry is important to allow those using it to feel comfortable through pool water being physically clean and biologically safe. Operators and health inspectors use test kits to ensure that pool water chemistry is correct and will not cause irritation or problems to both the patrons and the pool recirculation system. This study investigates the accuracy of the three commonly used pool test kits available in the market (Taylor, HACH, and ColorQ). Parameters tested are Free Available Chlorine (FAC), Total Chlorine (TC), and pH. Methods: Using artificial pool water with known concentrations of FAC, TC, and pH, 30 samples were taken for the three different parameter from the three test kits. The indicated concentrations and pH on the test kits were then recorded and used to compare with the known standards. Results were analyzed using the statistical software NCSS. One sample t-tests were performed to indicate whether or not the test kit as accurate in reading different parameters of pool chemistry. Results: Taylor Test Kit: Readings for FAC (2.6ppm) showed 2.4ppm, TC (2.7ppm) showed 2.43ppm, and pH (7.1) showed 7.1. HACH Test Kit: Readings for FAC (2.8ppm) showed 3.5ppm, TC (3.0ppm) showed 3.5ppm, and pH (7.1) showed 6.97. ColorQ Test Kit: Readings for FAC (2.7ppm) showed 3.0ppm, TC (3.0ppm) showed 3.0ppm, and pH (7.0) showed 6.96. Conclusion: All three test kits have accurate readings for pH levels. However, the test kits do not provide accurate readings for FAC and TC which would make it difficult to calculate CC in pool waters. Although the FAC and TC readings are inaccurate, they are able to provide operators and health inspectors with brief information regarding pool water chemistry.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".