Application of Evidence Theory to Quantify Uncertainty in Contaminant Transport Modeling
Bibliographic record
Abstract
Spatial variability and uncertainty in the hydrogeologic system make contaminant transport in the subsurface a complex phenomenon. Both random and non random uncertainties exist in contaminant transport modeling. An ideal approach is to use possibilistic distributions based on fuzzy sets for non random uncertainties and probabilistic distributions based on frequency of occurrence for random uncertainties. Thus, both probability and possibility distributions need to be incorporated. This paper presents a method using fuzzy-stochastic partial differential equations (FSPDEs) to simulate the uncertainties in contaminant fate and transport. In this paper, we use evidence theory, which can be related to both probability and possibility theory and is thus more suitable to quantify probabilistic and possibilistic uncertainties in contaminant transport modeling. Ranges of final results of FSPDEs, based on evidence theory, are presented in the form of upper and lower limits given by plausibility PlmaxT and belief function BelminT, respectively.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".