Using Water Isotope Tracers to Develop the Hydrological Component of a Long-Term Aquatic Ecosystem Monitoring Program for a Northern Lake-Rich Landscape
Bibliographic record
Abstract
Arctic lake-rich landscapes are vulnerable to climate change, but their remote locations present a challenge to develop effective approaches for monitoring hydroecological status and trends. Here, we structure the hydrological component of an aquatic ecosystem monitoring program that addresses concerns of Parks Canada (Vuntut National Park) and the Vuntut Gwitchin First Nation about changing water levels of Old Crow Flats (OCF), Yukon, Canada, a 5600-km2 thermokarst landscape recognized nationally and internationally for its ecological, historical, and cultural significance. The foundation of the monitoring program is 5 years (2007–2011) of water isotope data from 14 lakes situated in catchments that are representative of the land-cover and hydrological diversity of OCF. Isotopic compositions of input water (δI) and evaporation-to-inflow (E/I) ratios, calculated using the coupled-isotope tracer method, provide key hydrological metrics for each lake over the 5-year sampling interval. From these time series, we identify monitoring lakes that are sensitive to changes in snowmelt, rainfall, and evaporation, and demonstrate the use of the Mann-Kendall test for determining statistically significant trends in the roles of these hydrological processes on lake-water balances. These approaches will serve to identify lake hydrological responses to climate change and variability from ongoing water isotope monitoring by Parks Canada, in partnership with the Vuntut Gwitchin Government, Wilfrid Laurier University, and the University of Waterloo.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".