Uncertainty Analysis of Stochastic Solute Transport in a Heterogeneous Aquifer
Bibliographic record
Abstract
The uncertainty of predicting stochastic solute transport in an aquifer with heterogeneous hydraulic conductivity was quantified. Two sources of uncertainty were considered in the analysis including uncertainty that stems from inability to exactly predict the hydraulic conductivity at unmeasured locations and uncertainty that results from imperfect knowledge of the parameters in stochastic model. Hydraulic conductivity field was simulated using a random space function model while considering the nugget effect. The posterior distribution of parameters in the model was then obtained using Bayesian inference method of Markov Chain Monte Carlo (MCMC) Metropolis-Hastings (MH) algorithm. Inferred optimal parameter set was lastly used to generate conditional hydraulic conductivity fields to simulate solute transport in groundwater. As an illustrative example, a hypothetical steady two-dimensional flow in a heterogeneous aquifer was adopted. Results showed that the uncertainty of predicting solute transport in groundwater decreased when more conditional data were included, which was attributed to the fact that the optimal parameter value approached its hypothetical value in the posterior parameter distributions under the scenario of using more conditional data. Another important finding was that the degree of uncertainty for predictive variance is much higher in the area of higher solute concentration while the uncertainty for predictive absolute error shows no obvious trend when determining the distribution of solute concentration. We concluded that a balance may exist between global and local uncertainty for predictive absolute error. At last, the relative importance of parameter uncertainty to uncertainty of predictive solute transport was revealed. The variance and nugget ranked the top two important factors, followed by the expected value and the integral scale of the spatial stochastic process.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".