Carbon cycling and storage in a temperate freshwater marsh in eastern Ontario
Bibliographic record
Abstract
Freshwater marshes are underexplored yet significant carbon (C) sinks, playing a critical role in mitigating climate change.This thesis investigated C storage and cycling within a Typhadominated freshwater marsh at the Mer Bleue wetland complex in eastern Ontario, Canada.Field sampling during the 2022 growing season was conducted to quantify C pools across above-and belowground biomass, soil, and water.The results demonstrated that belowground biomass is the largest C stock, containing seven times more C per unit area than aboveground biomass.Soil C stocks were also substantial, with the mineral soil layer contributing a significant proportion due to its high clay content.Seasonal trends in dissolved organic carbon (DOC) concentrations suggested minimal downstream transport, indicating effective retention within the marsh.Analyses of isotopic signatures (δ 13 C and δ 15 N-nitrogen) and C:N ratios revealed limited decomposition at depth, with the mineral soil layer showing potential for long-term C stabilization.Contrary to expectations, soil C:N and soil δ 15 N did not consistently decrease with depth, highlighting unique decomposition and nitrogen dynamics in waterlogged soils.Additionally, the study underscored the need for refined bulk density measurements and integrated assessments of both organic and mineral soil layers to improve marsh C stock estimates.These findings contribute to the understanding of freshwater marsh ecosystems as dynamic C sinks, emphasizing their conservation and the importance of continued research to inform wetland management strategies under changing environmental conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".