Directional geostatistical simulation for regional hydrostratigraphic units uncertainty characterization, Innisfil Creek sub-watershed, Ontario
Bibliographic record
Abstract
The uncertainty characterization of hydrostratigraphic systems is important for risk assessment in hydrogeology. A complex system is difficult to characterize due to limited sampling. Geostatistical simulation methods aim at representing the underlying modeling uncertainties about hydrostratigraphic units proportions, properties and spatial arrangement through an ensemble of equally probable models. These models control the connectivity and, ultimately, the response of the system under an external stimulus. It is then critical to have geostatistical algorithms that can accurately reproduce the geological controls on the flow, such as the directional ordering of units. The latter is an important and common feature of sedimentary environments. Recent developments in geostatistics have improved the realism of hydrostratigraphic units simulation. In that regard, we revisited the Markovian Categorical Prediction (MCP) simulation method to allow for trends and directional ordering in simulation. The adapted MCP approach was applied to determine the uncertainty at a regional scale within the Innisfil Creek subwatershed with its 18 hydrostratigraphic units. The trend in unit occurrences is accounted for by locally confining the geostatistical algorithm to a deterministic model, without resorting to auxiliary variables. The presented methodology allows characterization of the uncertainty of the hydrostratigraphic model. The outputs are a suite (ensemble) of models, which provide a variability assessment of the spatial units arrangement and are an alternative to the deterministic one that complies with known data and unit ordering and trends. The alternative models propose a realistic variation for the thicknesses of the different units. Results suggest that the updated MCP methodology is highly flexible and efficient for closely replicating hydrostratigraphic units ordination, even for units occurring with low frequency. These geostatistical models are then appropriate for characterizing the uncertainty of groundwater flow and transport as well as aquifer vulnerability and the delineation of wellhead protection areas.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".