Constraining groundwater discharge in a large watershed: Integrated isotopic, hydraulic, and thermal data from the Canadian shield
Bibliographic record
Abstract
The objective of this study is to evaluate the pattern and rate of groundwater discharge in a large, regulated fractured rock watershed using novel and standard methods that are independent of base flow recession. Understanding the rate and pattern of groundwater discharge to surface water bodies is critical for watershed budgets, as a proxy for recharge rates, and for protecting the ecological integrity of lake and river ecosystems. The Tay River is a low‐gradient, warm‐water river that flows over exposed and fractured bedrock or a thin veneer of coarse‐grained sediments. Natural conservative (δ2H, δ18O, Cl, and specific conductance), radioactive (222Rn), and thermal tracers are integrated with streamflow measurements and a steady state advective model to delimit the discharge locations and quantify the discharge fluxes to lakes, wetlands, creeks, and the Tay River. The groundwater discharge rates to most surface water body types are low, indicating that the groundwater and surface water system may be largely decoupled in this watershed compared to watersheds underlain by porous media. Groundwater discharge is distributed across the watershed rather than localized around lineaments or high‐density zones of exposed brittle fractures. The results improve our understanding of the rate, localization, and conceptualization of discharge in a large, fractured rock watershed. Applying hydraulic, isotopic, or chemical hydrograph separation techniques would be difficult because the groundwater discharge “signal” is small compared to the “background” surface water inflows or volumes of the surface water bodies. Although this study focuses on a large watershed underlain by fractured bedrock, the methodology developed is transferable to any large regulated or unregulated watershed. The low groundwater discharge rates have significant implications for the ecology, sustainability, and management of large, crystalline watersheds.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".