Bedrock outcrops: A window for enhanced midwinter recharge
Bibliographic record
Abstract
As midwinter melt and rain-on-snow events become more common occurrences in the northern hemisphere under climate change, incorporating frozen processes when simulating winter-time recharge is increasingly necessary. The activation of infiltration pathways and recharge dynamics of shallow bedrock environments under frozen conditions has received relatively little attention. Over the 2019-2020 winter, hydrogeologic and cryospheric conditions of the surface, unsaturated, and saturated zones were monitored around a low-lying granitic outcrop in eastern Ontario, Canada. Interpretation of the data indicated that the soil-rock contact around outcrop margins was the key pathway enabling midwinter infiltration and recharge. To support this conceptual model and further explore the role of outcrops in enhancing midwinter bedrock recharge, a numerical investigation was undertaken. Measured climate data (hourly time step) was used to govern the surface energy and water balances of a 1D finite difference model that incorporates frozen processes. Measured snow depth, soil moisture content, and soil temperature profiles were simulated. Simulations with vertical infiltration alone could not account for observed increases in moisture content in the deepest soil horizons. This is attributed to additional lateral flow along the unfrozen soil-rock contact that bypasses the frozen soil layers. Preliminary results support the concept that bedrock outcrops provide a window for midwinter infiltration since repeated winter melts reduce frozen soil permeability and inhibits vertical infiltration until the ground thaws. Results from the surface/near-surface simulations are used to guide the development of a 2D finite element model that includes heat and flow transport and ground freeze-thaw. The impacts to bedrock recharge under different rainfall and snowmelt scenarios as well as various outcrop geometries are explored. Results from these numerical experiments help provide greater insight into the processes driving enhanced midwinter bedrock recharge under conditions of warmer winters.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".