Soil Organic Carbon in Prairie Pothole Wetlands: Assessing Variability and Stocks across Scales
Bibliographic record
Abstract
Prairie pothole wetlands exhibit highly variable soil organic carbon distributions that are driven by their unique hydrological and geomorphological characteristics as well as climate variables. These depression wetlands are a characteristic landscape feature that formed during the last glacial retreat in the Canadian Prairies and Upper Midwest United States. Land management practices, such as drainage and cultivation further influence these wetland soils through changes to the hydrology, vegetation type, and organic matter inputs. Understanding how these factors contribute to wetland soil organic carbon variability is essential for assessing their carbon storage contribution across scales, from individual wetlands in a field to the overall prairie pothole wetland landscape which spans over 770,000 km2. The objectives of this study were to 1) assess within wetland variability of soil organic carbon as for prairie pothole wetlands and 2) evaluate soil organic carbon stocks of these wetlands across a climate gradient as well as under crop production with or without drainage. Soil samples were collected from 134 prairie pothole wetlands to a depth of 1 m, at 3 landscape positions (centre, toeslope, and midslope). Soil classification was conducted at each sampling point and the soil cores were divided into 4 depth increments (0-15, 15-30, 30-60, and 60-100 cm) then measured for soil organic carbon as well as various other physiochemical properties. Our findings demonstrated that the within wetland variability can be explained by the wetland hydrology and historical tillage practices that led to soil erosion from upslope positions into the wetland depressions and consequently organic matter rich buried soil horizons at depths generally near 60 cm. Drained wetlands also showed evidence of soil organic carbon variability spatially and with depth due to soil redistribution that occurred during surface drainage installation. At the landscape scale, climate regime, wetland hydrology, parent material, and land management explained up to 39% of the wetland soil organic carbon variability. Based on this understanding we can more accurately estimate the soil organic carbon stock contribution of wetlands in agricultural landscapes and prioritize the sustainable management of these areas.
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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 | 0.001 |
| 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) | 0.000 | 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".