Use of water isotope tracers to characterize the hydrology of prairie wetlands in Alberta, Canada
Bibliographic record
Abstract
Spanning 750,000 km2 across the Northern Great Plains of North America, the Prairie Potholes Region (PPR) is characterized by millions of shallow wetlands, forming a unique ecosystem that provides habitat for wildlife, carbon storage, and flood control. However, the presence and persistence of Prairie Pothole wetlands is vulnerable to the increasing effects of climate change and land use. Knowledge of northern Great Plains prairie wetland hydrology is based primarily on a few long-term research stations, but generalizing from these intensively monitored areas to the entire PPR requires application of synoptic hydrological approaches across a spatially extensive dataset. The aim of this study is to assess the relative importance of input water sources and evaporative water loss on prairie pothole wetlands of varying permanence class in the Parkland and Grassland Natural Regions in Alberta (Canada). We compare a normal precipitation year (2014) and a relatively dry year (2015), as well as comparing natural sites to 24 restored wetlands. Water samples were collected at intervals during May-August of both years and analyzed for oxygen and hydrogen isotope composition. A combination isotope-mass balance and Bayesian approach was used to generate hydrologic metrics including the isotope composition of input water and evaporation to inflow ratio. Temporal synchrony was tested among the years and site types. We observed little difference in wetland hydrology between the Grassland and Parkland during a normal climate year, but under drier conditions Grassland sites were more vulnerable to evaporation. High synchrony across the study regions indicates that climate change will likely affect wetlands similarly, while restored sites may be less vulnerable to drawdown than natural sites. Interestingly, restored wetlands were more resistant to drawdown than natural wetlands in the Parkland, which will have consequences for wetland biota and climate change adaptation in the northwestern PPR.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".