Improved simulation of poorly drained forests using Biome-BGC
Bibliographic record
Abstract
Forested wetlands and peatlands are important in boreal and terrestrial biogeochemical cycling, but most general-purpose forest process models are designed and parameterized for upland systems. We describe changes made to Biome-BGC, an ecophysiological process model, that improve its ability to simulate poorly drained forests. Model changes allowed for: (1) lateral water inflow from a surrounding watershed, and variable surface and subsurface drainage; (2) adverse effects of anoxic soil on decomposition and nutrient mineralization; (3) closure of leaf stomata in flooded soils; and (4) growth of nonvascular plants (i.e., bryophytes). Bryophytes were treated as ectohydric broadleaf evergreen plants with zero stomatal conductance, whose cuticular conductance to CO(2) was dependent on plant water content. Individual model changes were parameterized with published data, and ecosystem-level model performance was assessed by comparing simulated output to field data from the northern BOREAS site in Manitoba, Canada. The simulation of the poorly drained forest model exhibited reduced decomposition and vascular plant growth (-90%) compared with that of the well-drained forest model; the integrated bryophyte photosynthetic response accorded well with published data. Simulated net primary production, biomass and soil carbon accumulation broadly agreed with field measurements, although simulated net primary production was higher than observed data in well-drained stands. Simulated net primary production in the poorly drained forest was most sensitive to oxygen restriction on soil processes, and secondarily to stomatal closure in flooded conditions. The modified Biome-BGC remains unable to simulate true wetlands that are subject to prolonged flooding, because it does not track organic soil formation, water table changes, soil redox potential or anaerobic processes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".