A fuzzy-parameterised stochastic modelling system for predicting multiphase subsurface transport under dual uncertainties
Bibliographic record
Abstract
A fuzzy-parameterised stochastic modelling system (FPSMS) was proposed in this study to investigate the impacts of uncertainties associated with hydrocarbon contaminant transport in subsurface. FPSMS integrated the multiphase numerical simulator, the fuzzy transformation method, and the Monte Carlo simulation technique into a general modelling framework, and was capable of dealing with coupled probabilistic–possibilistic uncertainties (in fuzzy-parameterised stochastic format). The simulation of light non-aqueous phase spill liquid (LNAPL) in an experimental system was used to demonstrate the applicability of the proposed method. Porosity and intrinsic permeability were considered as stochastic inputs with the means and standard deviations being characterised by fuzzy sets. The study results demonstrated that FPSMS was effective in evaluating the joint impacts of highly uncertain inputs on predictions of the LNAPL movements in subsurface. Compared with traditional fuzzy-stochastic analysis methods, FPSMS was suitable in tackling dual uncertainties, generating outputs with richer information, and even having more efficient calculation algorithms. Also, it could be a good reference for further risk assessment and remediation design for petroleum-contaminated sites.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".