Employing multi-criteria decision analysis to select sustainable point-of-use and point-of-entry water treatment systems
Bibliographic record
Abstract
Point-of-use (POU) and point-of-entry (POE) drinking water treatment systems are gaining prominence, particularly from the point-of-view of technical appropriateness and consumer acceptance. They are becoming an increasingly viable alternative for small water treatment systems or in individual homes. However, sustainability concerns have been voiced in a number of studies investigating these devices. In this paper, sustainability is examined with respect to the fulfillment of treatment systems for a set of technical, economic, environmental and socio-cultural objectives. Consequently, the use of a hierarchy of sustainability indicators to compare various POU and POE water treatment alternatives is proposed. The indicators' definitions, as well as calculation and normalization methods are explained. The paper also presents a decision model that is capable of selecting the most sustainable treatment option. The model employs the analytical hierarchy process (AHP) to help in the analysis of indicators' relative importance with regard to sustainability and to develop the indicators and criteria weights required for aggregating a sustainability score. The generated sustainability scores essentially level the playing field when comparing POU and POE systems for technical and economic appropriateness for a particular water treatment case, in addition to incorporating more difficult to quantify system traits, such as environmental and socio-cultural sustainability.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".