Carbon Stocks and Recent Rates of Carbon Sequestration in Nutrient‐Rich Freshwater Wetlands From Lake Simcoe Watershed (Southern Canada)
Bibliographic record
Abstract
Abstract We examined soil properties and accumulation rates over the last ca. 100 years in four swamps and four marshes in southern Canada where these wetlands are common but under increasing anthropogenic pressure. One 50‐cm long core was collected from each wetland and analyzed to determine bulk density (BD) and organic matter (OM). Lead‐210 and cesium‐137 dating were used to estimate sediment accumulation rates. In the datable portion of each core, we determined the organic carbon (Corg), nitrogen (N), Corg/OM, Corg/N, Corg density, Corg stocks, and Corg accumulation rates. All parameters but one—Corg accumulation rates—were significantly different in swamps compared to marshes and between seasonally wet soils and those regularly flooded. Since 1950, Corg stocks varied from 6 to 13.6 kg m−2 with ≈23% more Corg in swamps than in marshes. When hydrology and deeper soils where considered, Corg stocks in regularly flooded wetlands were 60% higher than those of seasonally wet systems, emphasizing the role of hydrology in building up C stocks long‐term. Our measurements are within the wide range reported for similar systems in North America. The average rate of Corg accumulation in the studied wetlands (112 ± 87 g m−2 yr−1) is ≈50% higher than that of oligotrophic/ombrotrophic peatlands, but ≈47% lower than in tidal marshes from eastern Canada. Our study lends support to the case for wetland restoration through re‐wetting as an important nature‐based climate solution for mitigation of GHG emissions in areas where they were drained for agriculture and other purposes.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".