Simulating the impact of glaciations on continental groundwater flow systems: 2. Model application to the Wisconsinian glaciation over the Canadian landscape
Bibliographic record
Abstract
A 3‐D groundwater flow and brine transport numerical model of the entire Canadian landscape up to a depth of 10 km is constructed in order to capture the impacts of the Wisconsinian glaciation on the continental groundwater flow system. The numerical development of the model is presented in the companion paper of Lemieux et al. (2008b). Although the scale of the model prevents the use of a detailed geological model, commonly occurring geological materials that exhibit relatively consistent hydrogeological properties over the continent justify the simplifications while still allowing the capture of large‐scale flow system trends. The model includes key processes pertaining to coupled groundwater flow and glaciation modeling, such a density‐dependent (i.e., brine) flow, hydromechanical loading, subglacial infiltration, isostasy, and permafrost development. The surface boundary conditions are specified with the results of a glacial system model. The significant impact of the ice sheet on groundwater flow is evident by increases in the hydraulic head values below the ice sheet by as much as 3000 m down to a depth of 1.5 km into the subsurface. Results also indicate that the groundwater flow system after glaciation did not fully revert to its initial condition and that it is still recovering from the glaciation perturbation. This suggests that the current groundwater flow system cannot be interpreted solely on the basis of present‐day boundary conditions and it is likely that several thousands of years of additional equilibration time will be necessary for the system to reach a new quasi‐steady state. Finally, we find permafrost to have a large impact on the rate of dissipation of high hydraulic heads that build at depth and capturing its accurate distribution is important to explain the current hydraulic head distribution across the Canadian landscape.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".