Development of an algorithm for sizing storage systems for rainwater harvesting
Bibliographic record
Abstract
This paper investigates the potential of a Rain Water Catchment System (RWCS) for meeting and/or supplementing the irrigation requirements for urban residential landscapes in a Canadian situation. In a specific usage of harvested rainwater for irrigation the urban landscape, the determination of the potential landscape area is best achieved through the spreadsheet-based water balance studies. Such computer based water balance programs invoke recursive approach to maximize the landscape area that can be irrigated by the rainwater catchment system by setting the design rainfall at a desired probability level, preferably at 50% (median value). The methodology developed in this paper was applied to size the rainwater storage systems for the city of Thunder Bay, Canada. The median roof catchment area was determined as 120 m2.and the capacity of an optimal RWCS was found to be approximately 1500 litres i.e., three tanks of 500 litres each that are commonly available in the market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".