Decision support system to select sustainable point-of-use/point-of-entry treatment systems (D4SPOUTS)
Bibliographic record
Abstract
Point-of-use (POU) and point-of-entry (POE) devices are, in some situations, considered to be a viable solution for drinking water suppliers and consumers alike to deal with site specific drinking water issues. This paper introduces a newly developed decision support system (DSS) that employs decision making techniques to select among the various devices based on their characterization and sustainability assessment. Careful illustration of the various aspects and components of the DSS is provided and the decision process is explained. Aspects of validity, usability and sensitivity analysis are demonstrated through a hypothetical case study for removing lead introduced in the distribution system of municipally treated drinking water. The output of the DSS helps to determine the more sustainable treatment devices which should have positive implications for the application of POU and POE devices. Other potential uses of the DSS are described to illustrate its versatility and usefulness. The DSS is not intended to replace common engineering practice in selecting POU and POE treatment systems, but rather to give support to the users by providing the necessary information about all possible solutions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".