Quantifying relative contributions of source waters from a subalpine wetland to downstream water bodies
Bibliographic record
Abstract
Abstract Subalpine regions of the Canadian Rocky Mountains are expected to experience continued changes in hydrometeorological processes due to anthropogenically mediated climate warming. As a result, fresh water supplies are at risk as snowmelt periods occur earlier in the year, and glaciers contribute less annual meltwater, resulting in longer growing seasons and greater reliance on rainfall to generate runoff. In such environments, wetlands are potentially important components that control runoff processes, but due to their location and harsh climates their hydrology is not well studied. We used stable water isotopes of hydrogen and oxygen (δ2H and δ18O), coupled with MixSIAR, a Bayesian mixing model, to understand relative source water contributions and mixing within Burstall Wetland, a subalpine wetland (1900 m a.s.l.), and the larger Burstall Valley. These results were combined with climate data from the Burstall Valley to understand hydrometeorological controls on Burstall Wetland source water dynamics over spatiotemporal timescales. Our results show that the seasonal isotopic patterns within Burstall Wetland reflect greater reliance on snowmelt in spring and rainfall in the peak and post‐growing season periods. We found a substantial degree of mixing between precipitation (rain and snow) and stored waters in the landscape, especially during the pre‐growing season. These findings suggest that longer growing seasons in subalpine snow‐dominated landscapes put wetlands at risk of significant water loss and increased evaporation rates potentially leading to periods of reduced runoff during the peak‐ growing season and in extreme cases, wetland dry out.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".