Spatially explicit simulation of peatland hydrology and carbon dioxide exchange: Influence of mesoscale topography
Bibliographic record
Abstract
Carbon dynamics in peatlands are controlled, in large part, by their wetness as defined by water table depth and volumetric liquid soil moisture content. A common type of peatland is raised bogs that typically have a multiple‐layer canopy of vascular plants over a Sphagnum moss ground cover. Their convex form restricts water supply to precipitation and water is shed toward the margins, usually by lateral subsurface flow. The hydraulic gradient for lateral subsurface flow is governed by the peat surface topography at the mesoscale (∼200 m to 5 km). To investigate the influence of mesoscale topography on wetness, evapotranspiration (ET), and gross primary productivity (GPP) in a bog during the snow‐free period, we compare the outputs of a further developed version of the daily Boreal Ecosystem Productivity Simulator (BEPS) with observations made at the Mer Bleue peatland, located near Ottawa, Canada. Explicitly considering mesoscale topography, simulated total ET and GPP correlate well with measured ET (r = 0.91) and derived gross ecosystem productivity (GEP; r = 0.92). Both measured ET and derived GEP are simulated similarly well when mesoscale topography is neglected, but daily simulated values are systematically underestimated by about 10% and 12% on average, respectively, due to greater wetness resulting from the lack of lateral subsurface flow. Owing to the differences in moss surface conductances of water vapor and carbon dioxide with increasing moss water content, the differences in the spatial patterns of simulated total ET and GPP are controlled by the mesotopographic position of the moss ground cover.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".