Unveiling Water Quality Insights by Exploring Intuitionistic Fuzzy TOPSIS in Multi-criteria Decision Analysis
Bibliographic record
Abstract
The groundbreaking study employs the Intuitionistic Fuzzy Sets-TOPSIS (IFT) model to systematically evaluate the Cauvery River's water quality.To properly handle the complexity of intuitionistic fuzzy sets, the method starts with building a decision matrix.The Analytic Hierarchy Process (AHP), which tackles the imprecision of evaluation indices, produces weight coefficients that are properly defined.This produces a weighted decision matrix that makes it easier to establish membership tiers for different states with regards to water quality.Water quality is mostly determined by the highest membership level at the top of the hierarchy.The tremendous precision of the procedure shows how useful it could be for upcoming evaluations of water quality.In order to boost robustness even more, the technique gains legitimacy and credibility through the integration of the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI).
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".