Drivers of high rates of carbon burial in a riverine-influenced freshwater marsh in the Lake Erie watershed of southern Ontario, Canada
Bibliographic record
Abstract
Freshwater marshes are prevalent and important stores of carbon. They bury carbon in deeper soils, although reported rates of carbon accumulation are significantly higher over recent (decadal) versus longer (centennial and millennial) timescales. Intrinsic organic matter degradation, long-term climatic and ecological changes, and recent anthropogenic impacts on sediment fluxes and organic matter production may have a role in explaining this discrepancy, yet remain poorly understood for freshwater marshes. We collected a 4-m core from a riverine-influenced marsh in the watershed of Big Creek which drains into Lake Erie in southern Ontario, Canada, and conducted radiometric dating, elemental analyses, and programmed pyrolysis for organic matter characterization. Over the past 5,710 calibrated years, burial of organic (on average 26 ± 34 g C m-2 yr-1) and inorganic (22 ± 25 g C m-2 yr-1) carbon fractions has resulted in high rates of carbon accumulation. We found that elevated recent rates of organic carbon accumulation are driven by fractions that have low thermal stability and are predominantly from aquatic sources. This type of organic carbon is buried intermittently in deeper marsh sediments and corresponds to major hydro-fluvial events (e.g., Nipissing highstands), which coincide with regional marsh development. We deduce that lower fractions of labile carbon in deeper soils reflect long-term degradation, which underscores the notion that high recent rates of carbon accumulation are generally not sustained over centuries and millennia. Our research demonstrates the importance of identifying various carbon fractions in understanding carbon burial in freshwater marsh soils, and informing marsh conservation.
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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".