Process-based modelling of long-term carbon dynamics in a temperate swamp peatland
Bibliographic record
Abstract
Temperate swamps hold substantial carbon (C) in their standing biomass and can potentially accumulate peat. In Southern Ontario, Canada, swamp peats are estimated to store ~1.1 Pg C, with this C accumulation supported by distinct hydroclimatic conditions. Previous studies on swamps C fluxes are mostly based on short-term (30 years) interactions and feedbacks that exist between temperate swamp C flux and biophysical conditions. In this study, we adopted a process-based model (CoupModel, www.coupmodel.com) to simulate daily plant processes, energy, water, and C fluxes in one of the most well-preserved swamps in Southern Ontario, Beverly Swamp, over a 40-year period (1983-2023). CoupModel reproduced the measured C flux and controlling variables with (coefficient of determination, R2) values of 0.75, 0.94 & 0.6 for soil respiration, surface soil temperature (0-5 cm) and water table depth, respectively. Analysis of the interrelationships (R2 values) between the simulated carbon flux and biophysical conditions showed that 88%, 51%, 31%, 68% of soil respiration rates were explained by soil surface temperature, soil volumetric moisture contents (0-30 cm), water table depth and gross primary productivity, respectively. Our model simulation showed the swamp’s C uptake capacity, as net ecosystem exchange, dwindled over the simulated period but it was a net C sink in most years. This decreasing trend can be attributed to warmer and drier conditions in the region, which may be exacerbated with future climate change predictions. Overall, the study shows that processed-based models (CoupModel) are effective tools for improving our understanding of long-term C dynamics of temperate forested wetlands and the interactions that exist between C flux components and abiotic conditions. This has implications for informed decision-making on the management of temperate swamp ecosystems and the C stored within them.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".