Land Management Strategies Influence Soil Organic Carbon Stocks of Prairie Potholes of North America
Bibliographic record
Abstract
Soil organic carbon (SOC) stocks of Prairie Pothole Region (PPR) wetlands in the central plains of Canada and the United States are highly variable due to natural variation in biota, soils, climate, hydrology, and topography. Land-use history (cropland, grassland) and land-management practices (drainage, restoration) also affect SOC stocks. We conducted a region-wide assessment of wetland SOC stocks using data from the Canadian and US portions of the PPR under various management types. Natural wetlands with no disturbance history in the wetland basin or surrounding catchment had considerably greater average SOC stocks in the upper (0–15 cm) soil profile than wetlands surrounded by cropland. Hydrologically restored wetlands did not show significantly greater SOC stocks than drained wetlands, but wetlands surrounded by restored grasslands did have greater SOC stocks in the upper soil profile than those surrounded by croplands. Similarities among cropped and restored wetlands likely were due to insufficient time since restoration, as well as high variability attributable to several environmental factors within the region. We conclude that avoided loss of natural wetlands from drainage and avoided loss of native grasslands from cropping have the most benefit for preserving wetland SOC stocks. Robust PPR SOC models that incorporate multiple abiotic, biotic, and land-use factors are required to determine where and when restoration is most effective for SOC sequestration.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".