Uncertainty Analysis of Stochastic Solute Transport in a Heterogeneous Aquifer
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
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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