Modeling seasonal to annual carbon balance of Mer Bleue Bog, Ontario, Canada
Bibliographic record
Abstract
Northern peatlands contain enormous quantities of organic carbon within a few meters of the atmosphere and play a significant role in the planetary carbon balance. We have developed a new, process‐oriented model of the contemporary carbon balance of northern peatlands, the Peatland Carbon Simulator (PCARS). Components of PCARS are (1) vascular and nonvascular plant photosynthesis and respiration, net aboveground and belowground production, and litterfall; (2) aerobic and anaerobic decomposition of peat; (3) production, oxidation, and emission of methane; and (4) dissolved organic carbon loss with drainage water. PCARS has an hourly time step and requires air and soil temperatures, incoming radiation, water table depth, and horizontal drainage as drivers. Simulations predict a complete peatland C balance for one season to several years. A 3‐year simulation was conducted for Mer Bleue Bog, near Ottawa, Ontario, and results were compared with multiyear eddy covariance tower CO 2 flux and ancillary measurements from the site. Seasonal patterns and the general magnitude of net ecosystem exchange of CO 2 were similar for PCARS and the tower data, though PCARS was generally biased toward net ecosystem respiration (i.e., carbon loss). Gross photosynthesis rates (calculated directly in PCARS, empirically inferred from tower data) were in good accord, so the discrepancy between model and measurement was likely related to autotrophic and/or heterotrophic respiration. Modeled and measured methane emission rates were quite low. PCARS has been designed to link with the Canadian Land Surface Scheme (CLASS) land surface model and a global climate model (GCM) to examine climate‐peatland carbon feedbacks at regional scales in future analyses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".