GA-Fuzzy Decision Support System for Mercury Removal in Natural Waters
Bibliographic record
Abstract
The idea of this research is to apply sustainability and augment efficiency of the aquatic systems by intelligent tools. This paper exploits fuzzy logic approach as a flexible methodology for providing supplementary information about mercury removal in natural waters. Fuzzy logic generates information on Hg behaviour in water according to its uptake by bio-species and adsorption by sediments. Fuzzy Decision Support System (FDSS) comprises knowledge base (i.e. premises and conclusions), fuzzy sets, and fuzzy rules. Knowledge base and rules are being built manually and by algo- rithm. GA-FDSS incorporates genetic algorithm GA to build optimal approximation for knowledge base, fuzzy sets, and rules. The role of integrating GA with FDSS is to train knowledge base and rules automatically from available data, hence FDSS models and predicts conclusion acquired. The findings of this research show more than 95% correlation between observed data and soft computed data. The optimal biological uptake occurs at pH of 5.5. The optimal sedi-ment adsorption occurs at pH of 8. The final mercury concentration calculated in natural waters is about 7 ? 10–8 mole/L. The results show that the removal efficiency of mercury by natural waters approaches 97%. Consequently the obtained fuzzy logic informative hierarchy is proficient to manage metals removal by aquatic systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".