Influence of Dynamic River Stage on The Vulnerability of Water Wells and Structure Foundations in Cold Regions
Bibliographic record
Abstract
Groundwater is important for people in Northern Canada, yet the groundwater protection protocols \nand water well vulnerability assessments designed for other warmer regions of Canada may not be \napplicable to communities in Northern Canada due to the unique hydrogeological characteristics \nthere. The seasonal melt of snow and ice leads to intense river stage fluctuations and might cause \nflooding issues for the adjacent floodplain; however, their influences on the vulnerability of drinking \nwater wells and nearby infrastructure are not fully understood. This research considers a case study at \nCarmacks, central Yukon, where an integrated surface-subsurface numerical modelling code was used \nto examine these processes. The model results indicate that the annual variation in the river stage \ntemporarily reverses the direction of the hydraulic gradient between the surface water body and the \nadjacent aquifer. Stream reaches that gain groundwater under lower river stages can become losing \nstreams during the high stage periods, which facilitates the transport of solute from the river to the \nadjacent aquifer. Additionally, the model shows that the annual river stage variation can temporarily \nalter the size and orientation of the region that contributes water to pumping wells, which means new \nenvironmental threats could become important. In terms of travel time, the model results suggest that \nannual river stage variation accelerates the transport of river-origin solutes to the adjacent aquifer, and \nhigher river peaks facilitate more rapid solute migration. Thus, the natural protection of the soil travel \npath against microbial pathogens may become inefficient and water wells may become more \nvulnerable. The model results also demonstrate that higher peak river stages are more likely to cause \nbasement inundation in buildings on the riverbank than average peaks, and that the duration of \nbasement inundation varies at different locations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".