An Evaluation on the Use of Ultra Low-Flow Toilets In Four Developing Countries as a Means to Contribute to Water Resource Sustainability
Bibliographic record
Abstract
This paper is an investigation into the use of water-saving toilets by developing countries to clarify whether their application can help promote water resource sustainability. To begin this evaluation, theoretical data collection is made of toilet water-use prior to and after the hypothetical installation of these conserving toilets to determine quantitative statistics between each chosen country in the analysis of water conserved. By determining each country’s theoretical percent of water usage savings and comparing these results between the various countries, an evaluation as to the affect these ultra low-flow toilets (ULF) have on water conservation is made. Secondly, an evaluation from this data is made on the impact each country had on water usage and therefore the percentage of savings it contributed towards water conservation. Lastly, a comparison between each country was theoretically presented to infer individually and collectively the contribution possible towards global water resource sustainability. Australia was chosen to be in this evaluation because it is a country experiencing severe drought conditions and one which could benefit profoundly due to its’ inherent environment. The United States was chosen because of their enormous water usage and ensuing, diminishing supply. The remaining countries, Canada and the United Kingdom, were chosen randomly of data available between the remaining developed countries with a high percentage of domestic water usage. The research that supports ULF toilets as a feasible means to conserve water and therefore have more water available for delivery is supported by studies, yet there is still controversy over the efficient design of these toilets and the 1992 U.S. Energy and Conservation Act mandated for ULF toilets to be installed. 1 This paper addresses the validity of this opposition as well as evaluates the contribution toilets could make towards water resource sustainability, especially when the engineering design is adequate. The purpose of this paper is to confirm a need for engineering education and industry to address this issue jointly and how their involvement could contribute towards global water resource sustainability and world peace.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".