Effects of non‐Gaussian copula‐based hydraulic conductivity fields on macrodispersion
Bibliographic record
Abstract
This is an application of a spatial copula model that is fitted to a real world data set. The copula model allows modeling of pure spatial dependence independently of the marginal distribution. Using non‐Gaussian copula models it is demonstrated that the spatial dependence structure of the Borden aquifer is significantly non‐Gaussian—despite the fact that the Borden aquifer is commonly thought of as a relatively homogeneous porous medium with a small variance of hydraulic conductivity. In addition to evaluating the spatial dependence structure of the Borden hydraulic conductivity data set using copulas, goal of this study is to explore if the structure of the hydraulic conductivity field influences a physical property, such as plume evolution as evaluated by second spatial central moments of concentration fields. For this comparison, two types of hydraulic conductivity fields were fitted to the Borden hydraulic conductivity data set: one with a Gaussian and the other with a non‐Gaussian type of dependence. These two types of hydraulic conductivity fields were constructed such that their second‐order spatial moments are identical, and hence they cannot be distinguished by semivariogram‐based geostatistics. This paper illustrates that the spatial dependence structure of the Borden hydraulic conductivity data set is significantly non‐Gaussian. Despite the fact that Borden is a relatively homogeneous porous medium, and despite the fact that both types of spatial fields are not distinguishable by their variograms, the solute transport characteristics based on these two types of isotropic fields differ significantly in two‐dimensional settings. The difference is less pronounced in three‐dimensions with anisotropy. It is postulated that non‐Gaussian spatial dependence of hydraulic conductivity and a more skewed marginal distribution of hydraulic conductivity will have significant implications in the other more heterogeneous aquifers.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".