Evaluating topography‐based predictions of shallow lateral groundwater discharge zones for a boreal lake‐stream system
Bibliographic record
Abstract
Abstract Groundwater discharge along streams exerts an important influence on biogeochemistry and thermal regimes of aquatic ecosystems. A common approach for predicting locations of shallow lateral groundwater discharge is to use digital elevation models (DEMs) combined with upslope contributing area algorithms. We evaluated a topography‐based prediction of subsurface discharge zones along a 1500 m headwater stream reach using temperature and water isotope tracers. We deployed fiber‐optic distributed temperature sensing instrumentation to monitor stream temperature at 0.25 m intervals along the reach. We also collected samples of stream water for the analysis of its water isotope composition at 50 m intervals on five occasions representing distinct streamflow conditions before, during, and after a major rain event. The combined tracer evaluation showed that topography‐predicted locations of groundwater discharge were generally accurate; however, predicted magnitude of groundwater inflows estimated from upslope contributing area did not always agree with tracer estimates. At the catchment scale, lateral inflows were an important source of streamflow at base flow and peak flow during a major rain event; however, water from a headwater lake was the dominant water source during the event hydrograph recession. Overall, this study highlights potential utility and limitations of predicting locations and contributions of lateral groundwater discharge zones using topography‐based approaches in humid boreal regions.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".