A Synthesis of Terrestrial Carbon Balance of Alaska and Projected Changes in the 21st Century: Implications for Climate Policy and Carbon Management
Bibliographic record
Abstract
To better understand how carbon responses to changes in climate and other drivers in Alaska might influence national climate and carbon management policies, the U.S. Geological Survey, in collaboration with the USDA Forest Service and university scientists, has conducted a comprehensive assessment of the historical (1960-2009) and projected (2010-2099) carbon balance for Alaska. This assessment of carbon dynamics in Alaska includes (1) syntheses of soil, vegetation, and surface water carbon stocks and fluxes in Alaska, and (2) state of the art models of fire dynamics, vegetation change, forest management, permafrost dynamics, and upland, wetland, and surface water ecosystem carbon dynamics. Here we report on progress in the soils synthesis, fire and vegetation dynamics synthesis, and syntheses of upland, wetland, and inland waters components. The terrestrial reporting regions for soil, upland, and wetland components of this assessment are based on the four large terrestrial Landscape Conservation Cooperatives (LCC) in Alaska: (1) the Arctic, (2) the Western Alaska, (3) the Northwest Boreal, and (4) the North Pacific. The reporting regions for the inland waters’ component of this assessment are based on the six main hydrologic regions of Alaska: the Southeast, the South-Central, Southwest, Yukon, Northwest and Arctic Slope.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".