Application of geochemical and groundwater data to predict sinkhole formation in a gypsum formation in Manitoba, Canada
Bibliographic record
Abstract
Numerical modelling approaches were used to investigate coupled groundwater flow and reactive transport processes in gypsum karst sub-terrain. A regional equipotential map and steady-state flow model were created using scarce data to gain insights into flow patterns and identify potential areas at risk for cavity and sinkhole development in a shallow gypsum formation. Coupled flow and reactive transport modelling was used to simulate the dissolution of gypsum between a sinkhole in a man-made drainage ditch and a quarry, where freshwater enters the drainage ditch and flows toward the quarry. Field data from a tracer test were used to characterize flow in the study area. The resulting regional equipotential map was valuable in identifying potential areas of sinkhole development; sinkholes occurred in areas underlain by thick gypsum formations with high flow gradients and radial flow. The reactive transport model was valuable in identifying the growth of the cavity and the timeline for the potential risk to road infrastructure. The reactive transport model indicated that cavity growth could be slowed by removing the inflow of freshwater into the drainage ditch. Groundwater equipotential maps, flow models and reactive transport models are valuable tools for the investigation of sub-terrain karst development including cavity development and sinkhole formation in evaporite minerals.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".