Estimated Carbon Sequestration in a Temperate Forest in Idaho of USA
Bibliographic record
Abstract
Assessing carbon (C) sequestration in forest ecosystems is fundamental to supply information to monitoring, reporting and verification (MRV) for reducing deforestation and forest degradation (REDD). The spatially-explicit version of Forest-DNDC (FDNDC) was evaluated using plot-based observations from Nez Perce-Clearwater National Forest (NPCNF) in Idaho of United States and used to assess C stocks in about 16,000 km2. The model evaluation indicated that the FDNDC can be used to assess C stocks with disturbances in this temperate forest with a proper model performance efficiency and small error between observations and simulations. Aboveground biomass in this forest was 85.1 Mg C ha-1 in 2010. The mean aboveground biomass in the forest increased by about 0.6 Mg C ha-1 yr-1 in the last 20 years from 1990 to 2010 with spatial mean stand age about 98 years old in 2010. Spatial differences in distributions of biomass, net primary production and net ecosystem product are substantial. The spatial divergence in C sequestration is mainly associated with the spatial disparities in stand age due to disturbances, secondly with ecological drivers and species. Climate variability and change can substantially impact C stocks in the forest based on the climatic variability of spatial climate data for a 33-year period from 1981 to 2013. Temperature rise can produce more biomass in NPCNF, but biomass cannot increase with an increase in precipitation in this forest. The simulation with disturbances using observations and estimates for the time period from 1991 to 2011 showed the effects of disturbances on C stocks in forests. The impacts of fires and insects on C stocks in this forest are highly dependent on the severity, the higher, the more C loss to atmosphere due to fires, and the more dead woods produced by fires and insects. The rates of biomass increase with an increase in stand age are different among the species. The changes in forest C stocks in the forest are almost species specific, non-linear and complex. The increase in aboveground biomass with an increase in stand age can be described by a high-order polynomial.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".