FVSBGC: a hybrid of the physiological model STAND-BGC and the forest vegetation simulator
Bibliographic record
Abstract
The physiological model STAND-BGC was linked to the forest vegetation simulator (FVS) as a system extension. With the linked model, an FVS user can invoke STAND-BGC to obtain tree- and stand-level physiological output in addition to standard FVS mensurational output. An FVS user may choose to have increments in diameter, height, crown ratio, and mortality from STAND-BGC replace those generated by FVS. This option essentially replaces the empirical growth engine of FVS with the physiological engine from STAND-BGC. Physiological and mensurational outputs were generated for an existing, fully stocked, Pinus contorta Dougl. ex Loud. stand, with and without thinning, using the hybrid model. The STAND-BGC engine produced results similar to FVS for the unthinned stand but predicted more rapid tree growth than FVS following thinning. Simulations for a newly regenerated stand using the linked model allowed assessment of the predicted effects of grass competition and drought on stand production. Comparisons of model predictions to remeasured permanent plot data showed the empirical and process growth engines had similar precision, but that STAND-BGC substantially overpredicted growth, while FVS slightly underpredicted growth. The need for model calibration and opportunities for more sophisticated communication between models is discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".