Stochastic hydrogeological modelling workflow in a glacial sedimentary basin, southern Ontario
Bibliographic record
Abstract
The focus of this study is to explore and assess the uncertainty related to geological heterogeneity in regional groundwater flow models. A stochastic approach is adopted in which the hydrostratigraphic architecture and respective hydrofacies variability directly controls the three-dimensional hydraulic conductivity (K) field. The following procedural steps were applied to develop the stochastic flow model: 1) generation of multiple equivalent hydrostratigraphic models honouring observed hydrostratigraphic units and stratigraphic transitions; 2) determination of the statistical scalar K distribution for each hydrostratigraphic unit along with the respective spatial correlation at the local-scale; 3) upscaling of the scalar K field (quasi-point) to its full 3-D tensor at the regional scale (block); 4) determination of the K-tensor structures and simulation of the K-tensor for hydrogeological models; 5) calibration of the models by numerical inversion of the K-tensor and preserving their component correlations including the recharge rate; and 6) quantification of the uncertainties in the groundwater flow model. The capability and performance of the suggested workflow is illustrated using a case study in southern Ontario. Results highlight the efficiency of the method in assessing model uncertainties and their impact on regional groundwater flow models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".