A fuzzy multi-criteria decision analysis approach for the management of petroleum-contaminated sites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents an effective Fuzzy Multi-Criteria Decision Analysis (FMCDA) approach for contaminated site management. The development was based on: selecting eight criteria for remedial alternative evaluation and determining the weight of criteria importance under uncertainty; addressing various uncertainty issues in remedial alternative assessment process using a fuzzy-set approach based on the questionnaire survey results; evaluating and ranking remedial alternatives by establishing a fuzzy evaluation matrix of remediation alternatives through comprehensively considering different stakeholder opinions and uncertainties within a general decision analysis framework. A case study on the remedial alternative selection for a contaminated site in western Canada is conducted to illustrate the efficiency and applicability of the developed approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it