Reimplementation of the Biome-BGC model to simulate successional change
Bibliographic record
Abstract
Biogeochemical process models are increasingly employed to simulate current and future forest dynamics, but most simulate only a single canopy type. This limitation means that mixed stands, canopy succession and understory dynamics cannot be modeled, severe handicaps in many forests. The goals of this study were to develop a version of Biome-BGC that supported multiple, interacting vegetation types, and to assess its performance and limitations by comparing modeled results to published data from a 150-year boreal black spruce (Picea mariana (Mill.) BSP) chronosequence in northern Manitoba, Canada. Model data structures and logic were modified to support an arbitrary number of interacting vegetation types; an explicit height calculation was necessary to prioritize radiation and precipitation interception. Two vegetation types, evergreen needle-leaf and deciduous broadleaf, were modeled based on site-specific meteorological and physiological data. The new version of Biome-BGC reliably simulated observed changes in leaf area, net primary production and carbon stocks, and should be useful for modeling the dynamics of mixed-species stands and ecological succession. We discuss the strengths and limitations of Biome-BGC for this application, and note areas in which further work is necessary for reliable simulation of boreal biogeochemical cycling at a landscape scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".