Modeling Temperature Fluctuations during Intermittent Water Usage within Water Systems: Water Quality Impact
Bibliographic record
Abstract
Temperature is a crucial factor that can influence chemical and microbiological activities within building water systems.Due to factors like widespread water conservation programs or shutdowns resulting from events like the COVID-19 pandemic, water stagnation in these systems can escalate, impacting water temperature.By integrating EPANET-MSX with field data, this study seeks to simulate and analyze spatial and temporal fluctuations in water temperature and microbial growth resulting from temperature variations.The simulated temperature data and Legionella concentrations at three points are compared with field data during a period of three weeks.Overall, the modeled showerhead temperatures show good alignment with the monitored data, although underestimations occur in specific locations and time periods.The comparison between actual Legionella measurements and simulated concentrations, considering only temperature effects, demonstrates better alignment with field data for daily flushing showers.However, as stagnation increases, discrepancies between the modeled data and actual measurements suggest that other factors, such as available nutrients, may limit growth.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".