A framework for selecting POU/POE systems
Bibliographic record
Abstract
Although the acceptance of point‐of‐use (POU) and point‐of entry (POE) systems is still being debated, it is generally acknowledged that the systems have a role to play in drinking water treatment. Certified systems being marketed today incorporate proven technologies that have been engineered to achieve defined contaminant removal targets. Although this is of paramount importance, there is value in assessing the sustainability of such treatment alternatives. This article investigates issues related to the implementation, management, and environmental effects of POU/POE systems and presents a framework for sustainability assessment of those systems. A set of sustainability criteria—technical, economic, environmental, and sociocultural—is defined. Quantitative and qualitative indicators are proposed to promote the practical use of these criteria for comparing and selecting among POU/POE systems. Survey results of experts' judgment on the effectiveness of the developed indicators are presented.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".