Simulation of boreal black spruce chronosequences: Comparison to field measurements and model evaluation
Bibliographic record
Abstract
This study used the Biome Biogeochemical Cycles (Biome‐BGC) process model to simulate boreal forest dynamics, compared the results with a variety of measured carbon content and flux data from two boreal chronosequences in northern Manitoba, Canada, and examined how model output was affected by water and nitrogen limitations on simulated plant production and decomposition. Vascular and nonvascular plant growth were modeled over 151 years in well‐drained and poorly drained forests, using as many site‐specific model parameters as possible. Measured data included (1) leaf area and carbon content from site‐specific allometry data, (2) aboveground and belowground net primary production from allometry and root cores, and (3) flux data, including biometry‐based net ecosystem production and tower‐based net ecosystem exchange. The simulation used three vegetation types or functional groups (evergreen needleaf trees, deciduous broadleaf trees, and bryophytes). Model output matched some of the observed data well, with net primary production, biomass, and net ecosystem production (NEP) values usually (50–80% of data) within the errors of observed values. Leaf area was generally underpredicted. In the simulation, nitrogen limitation increased with stand age, while soil anoxia limited vascular plant growth in the poorly drained simulation. NEP was most sensitive to climate variability in the poorly drained stands. Simulation results are discussed with respect to conceptual issues in, and parameterization of, the Biome‐BGC model.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".