Soil hydrodynamics and controls in prairie potholes of central Canada
Bibliographic record
Abstract
The soil moisture contents of the upland soils surrounding prairie wetlands are significant in regulating processes (e.g. infiltration, run‐off and evapotranspiration) that control the prairie wetlands' water balances and fluctuations. In this research, the soil moisture data were collected with Time Domain Reflectometer (TDR) and their distributions were characterised across sites with varying climatic regime, basins with varying wetland classes and various topographic positions, during the snow‐free period. The soil moisture contents of basins were variable among regions of Prairie Potholes Region (e.g. Hartt = 0.35 m3 m−3 and Old Wives = 0.18 m3 m−3) and their variability was related to regional Precipitation–Potential Evapotranspiration (P‐PET) gradient, particularly for the northern sites (r2= 0.98). Additionally, the soil moisture statuses was temporally variable during the snow‐free period (e.g. June = 0.27 m3 m−3 and August = 0.17 m3 m−3) and the variability displayed considerable relationship with daily P‐PET (r2= 0.63). At a local scale, the soil moisture content of the basins were structured by wetland class, position within the prairie basin (uplands = 0.17 m3 m−3 and lowlands = 0.27 m3 m−3), reflective of topographic effects, and the presence and permanence of wetlands (e.g. Class I = 0.18 m3 m−3, Class II = 0.21 m3 m−3, Class III = 0.22 m3 m−3 and Class IV = 0.24 m3 m−3).
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".