Boreal forest ecosystem dynamics. II. Application of the model to four vegetation types in interior Alaska
Bibliographic record
Abstract
The Spatial Alaskan Forest Ecosystem Dynamics (SAFED) model was validated across four of the most common vegetation types found in interior Alaska. The vegetation types were an alder (Alnus spp.) - balsam poplar (Populus balsamifera L.) site (FP2), an old-growth balsam poplar and white spruce (Picea glauca (Moench) Voss) site (FP3), a mixed deciduous (primarily birch (Betula papyrifera Marsh.) and aspen (Populus tremuloides Michx.)) and white spruce site (UP2), and a mature white spruce site (UP3). The FP site types are common on the floodplain along the Tanana River and the UP site types are common in the uplands in interior Alaska. SAFED is based on nitrogen productivity for vegetation growth, litter fall quantity and quality, and microbial efficiency for forest floor decomposition. The state factors (climate, topography, and disturbance) are used to describe a broad-scale classification of the landscape to define basic limitations for the driving variables. Climate and ecosystem-level disturbances are handled as restricted stochastic processes. The model has been programed in a spatial framework as an ARC/INFO AML within the GRID package. The current version of the model has been validated as functional from an individual tree basis (1-m2 cell size) in a number of forest types found in interior Alaska. The growth, litter fall, and forest floor decomposition were compared with data from the sites. An estimate of yearly carbon balance for the four sites was calculated.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".