Drivers of high rates of carbon burial in a riverine-influenced freshwater marsh in the Long Point Walsingham Priority Place of southern Ontario
Bibliographic record
Abstract
Reported rates of soil organic carbon (SOC) accumulation in wetlands are markedly higher over recent versus longer timescales, caused by SOC losses through decomposition, paleoenvironmental changes, and recent increases in sedimentation or biomass production. Explaining changes in SOC sequestration rates and determining the time horizon over which high rates are sustained are both critical for accurately measuring the potential for wetland conservation as a natural climate solution. Here, we present analyses on a 4-m core from a riverine-influenced marsh in Big Creek watershed, southern Ontario, to track changes in SOC accumulation regimes. Since wetland initiation ∼5700 years ago, mean long-term (pre-industrial) rates of SOC accumulation were 24 g C m −2 year −1 , and recent rates up to four times higher. We demonstrate that elevated recent rates of SOC accumulation are largely explained by more labile carbon in surficial soils, and are sustained for less than a century before transitioning to slower burial rates of predominantly recalcitrant organic matter. However, there are exceptions to this trend, such as when labile SOC was buried intermittently during Holocene Lake Erie highstands. Our research underscores the importance of organic matter type and hydroclimatic context in predicting long-term potential for marsh soils to stabilize atmospheric carbon.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".