Integrated Fuzzy-Stochastic Modeling of Petroleum Contamination in Subsurface
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
An integrated approach associated with fuzzy set theory, Monte Carlo simulation, and interval analysis are proposed in this study to address the uncertainties in simulating petroleum contamination in the subsurface. A numerical multiphase compositional modeling technique is implemented to examine the fate of petroleum contaminants in groundwater. The intrinsic permeability, longitudinal dispersivity, and soil porosity are considered as uncertain input parameters. A three-dimensional (3D) case of a petroleum contamination problem is presented to illustrate the suitability and capability of the proposed methods for managing uncertainties. The results show that the uncertainties in intrinsic permeability and porosity will have significant impacts on the modeling outputs. Neglecting these uncertainties may result in an unreasonable estimation of the contaminant fate in the subsurface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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