Global parameter sensitivity analysis of modelling water, energy and carbon dynamics in a temperate swamp
Bibliographic record
Abstract
Abstract. Forested peatlands cover a land area of 7 x 105 km2 and store ~77 Pg C in Canada. However, the carbon (C) cycling of forested peatlands, particularly swamps, has been understudied. Few modelling studies have been done on temperate swamp C cycling partly because of the scarcity of field measurements in this ecosystem. These gaps create uncertainties in modelling the C dynamics of temperate swamps and consequently limit our understanding of this ecosystem. To improve our understanding of the processes, interactions and feedbacks that mediate temperate swamp C cycling, we simulated the long-term (40 years) plant processes, energy, water and C fluxes of Beverly Swamp, a well-preserved swamp in Southern Ontario using a process-based model (CoupModel). CoupModel v6 was systematically calibrated for Beverly Swamp using the Generalized Likelihood Uncertainty Estimate (GLUE) method and validated with field measurements. The GLUE approach and its multicriteria constraints reduced the uncertainties associated with the modelling process and reasonably improved some of the simulation outcomes when compared to the initial single run and prior uniform distribution. Global sensitivity analysis of the parameters identified the important parameters that greatly influence temperate swamp C flux simulations and the interconnections that exist between simulated variables and parameters. Plant-related processes and hydrological variables exerted the strongest control on soil respiration simulation. However, these dynamics may be altered as climate continues to warm in coming decades. Results from this study provide valuable knowledge for predicting the fate of swamp C cycle in the region under a changing climate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".