Analytic Network Process for Developing Relative Weight of Wastewater Treatment Technology Selection
Bibliographic record
Abstract
Selecting the best wastewater treatment (WWT) technology requires a thorough qualitative and quantitative evaluation of multi-dependence criteria. A network based method is one of the many possible techniques that able to handle multi-dependence criteria in the selection. This paper proposes relative importance weights of alternatives in selecting the WWT technology using the analytic network process (ANP) in Terengganu Malaysia. The ANP is applied to establish the relative weights of alternatives based on criteria and sub-criteria that available in the WWT technology selection. Two faculty members attached to a public university and an engineer in Malaysian government agency were interviewed to provide evaluation within the framework of ANP. Inner dependence and outer dependence analysis of ANP are fully utilised to establish relative importance weights of alternatives. The experiment result reveals that the relative importance weights of the three alternatives are 0.3074, 0.2795 and 0.2447. The alternative ‘Composting’ has decided as the most suitable technology in WWT which provides the highest relative importance weight among all the three alternatives. The results would be a great significance for the practical implementation of the WWT technology selection.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".