Carbon balance of the taiga forest within Alaska: present and future
Bibliographic record
Abstract
Forest biomass, rates of production, and carbon dynamics are a function of climate, plant species present, and the structure of the soil organic and mineral layers. Inventory data from the U.S. Forest Service (USFS) Inventory Analysis Unit was used to develop estimates of the land area represented by the major overstory species at various age-classes. The CENTURY model was then used to develop an estimate of carbon dynamics throughout the age sequence of forest development for the major ecosystem types. The estimated boreal forest area in Alaska, based on USFS inventory data is 17 244 098 ha. The total aboveground biomass within the Alaska boreal forest was estimated to be 815 330 000 Mg. The CENTURY model estimated maximum net ecosystem production (NEP) at 137, 88, 152, 99, and 65 g·m 2 ·year 1 for quaking aspen (Populus tremuloides Michx.), paper birch (Betula papyrifera Marsh.), balsam poplar (Populus balsamifera L.), white spruce (Picea glauca (Moench) Voss), and black spruce (Picea mariana (Mill.) BSP) forest stands, respectively. These values were predicted at stand ages of 80, 60, 41, 68, and 100 years, respectively. The minimum values of NEP for aspen, paper birch, balsam poplar, white spruce, and black spruce were 171, 166, 240, 300, and 61 g·m 2 ·year 1 at the ages of 1, 1, 1, 1, and 12, respectively. NEP became positive at the ages of 14, 19, 16, 13, and 34 for aspen, birch, balsam poplar, white spruce, and black spruce ecosystems, respectively. A 5°C increase in mean annual temperature resulted in a higher amount of predicted production and decomposition in all ecosystems, resulting in an increase of NEP. We estimate that the current vegetation absorbs approximately 9.65 Tg of carbon per year within the boreal forest of the state. If there is a 5°C increase in the mean annual temperature with no change in precipitation we estimated that NEP for the boreal forest in Alaska would increase to 16.95 Tg of carbon per year.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".