Isotopic evidence for widespread cold‐season‐biased groundwater recharge and young streamflow across central Canada
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Transformations of precipitation into groundwater and streamflow are fundamental hydrological processes, critical to irrigated agriculture, hydroelectric power generation, and ecosystem health. Our understanding of the timing of groundwater recharge and streamflow generation remains incomplete, limiting our ability to predict fresh water, nutrient, and contaminant fluxes, especially in large basins. Here, we analyze thousands of rain, snow, groundwater, and streamflow δ 18 O and δ 2 H values in the Nelson River basin, which covers 1.2 million km 2 of central Canada. We show that the fraction of precipitation that recharges aquifers is ~1.3–5 times higher for precipitation falling during cold months with subzero mean monthly temperatures than for precipitation falling during warmer months. The near‐ubiquity of cold‐season‐biased groundwater recharge implies that changes to winter water balances may have disproportionate impacts on annual groundwater recharge rates. We also show that young streamflow—defined as precipitation that enters a river in less than ~2.3 months—comprises ~27% of annual streamflow but varies widely among tributaries in the Nelson River basin (1–59%). Young streamflow fractions are lower in steep catchments and higher in flatter catchments such as the transboundary Red River basin. Our findings imply that flat, lower permeability, heavily tiled landscapes favor more rapid transmission of precipitation into rivers, possibly mobilizing excess soluble fertilizers and exacerbating eutrophication events in Lake Winnipeg.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it