Coefficient of Variation of Drinking Water Networks in Residential Urban Complexes
Bibliographic record
Abstract
The objective of this paper is to establish a real value of daily and hourly variation coefficients for the design of drinking water networks. Determining the real daily K1 and hourly K2 coefficients of variation of different residential urban complexes, can constitute a reference for the design of future constructions. To be able to size drinking water networks, it is important to know the magnitude of variations in the volume of water that exists and the demands of the population, as the water consumption depends on various conditions and aspects such as: climate, socioeconomic factors population, and for future designs, the global Covid 19 pandemic. The study area will take place in Ecuador, located in the Guayas Province, The Daule city, specifically in the residential urban complexes and called citadels, in the Brillante stage in La Joya and Estelar stage in Villaclub. The macro water meter reading was recorded, 3 days a week, 24 hours a day, in each citadel, then, information was obtained from real water payroll to calculate the coefficients and the impact of the diameter was also calculated with the formula of Hazen Williams. Finally, some real values of coefficients, are not within the range established by current regulations.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".