Sizing of Rainwater Storage Units for Green Building Applications
Bibliographic record
Abstract
Green building design principles advocate the use of rainwater storage units to collect roof runoff during nonwinter seasons for landscaping, hardscape cleaning, and/or maintenance purposes, either in the form of rain barrels for smaller scale applications or cisterns for larger scale applications. This not only saves water which would otherwise be supplied from municipal water distribution systems but also reduces storm-water runoff which would otherwise be handled through urban storm-water management systems. The size of the storage units needs to be commensurate with the area of the roof and the desired water use rate. The local climate has an influence on the required size and achievable use rate as well. In this paper, analytical formulas are derived to estimate the required rainwater storage volume as a function of desired water use rate, reliability and local climate. In deriving these formulas, local climate characteristics are represented by probabilistic models and incorporated into the stochastic description of storage unit operating procedures and requirements. The resulting formulas may be used by engineers, architects, municipal governments, and storage unit manufactures for the estimation or recommendation of suitable rainwater storage unit sizes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".