Development of fuzzy multi-criteria decision analysis approach for contaminated site management.
Bibliographic record
Abstract
Selection of a proper remediation alternative is an important task in the decision making process of contaminated site management. The number of available remediation alternatives is increasing over the years as a result of perpetual development in scientific research. Decision makers face a confounded situation to select the best acceptable alternative by satisfying various preferences of different stakeholders (e.g., industry, government, public_. In this research, a fuzzy multi-criteria decision analysis (FMCDA) approach was developed. Since most information available in the decision making process is not deterministic, fuzzy-set theory was used to deal with such uncertainty. The developed FMCDA approach ranks the candidate alternatives according to the utility value which then assists decision makers in selecting more proper remediation options. Different stakeholders' opinions were effectively incorporated in the developed approach, allowing for a robust decision making for contaminated site management. A user friendly decision support system based on the FMCDA approach was also developed in this research. The developed method was then applied to the management of a site in northern British Columbia to examine its applicability. As well, existing multi-criteria decision making methods were also applied to the remediation selection of this site. The results suggest that the developed FMCDA method is more capable of considering uncertainty issues and it is a helpful means of integrating various interests from different stakeholders.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".