Relationship between Net Primary Productivity and Forest Stand Age under Different Site Conditions and Its Implications for Regional Carbon Cycle Study
Bibliographic record
Abstract
Net primary productivity (NPP) is a key component in the terrestrial ecosystem carbon cycle, and it varies according to stand age and site class index (SCI) for different forest types. Here we report an improved method for describing the relationships between NPP and stand age at various SCI values for the main forest types and groups in Heilongjiang Province, China, using existing yield tables, biomass equations, and forest inventory data. We calculated NPP as the sum of four components: Annual accumulation of live biomass, annual mortality of biomass, foliage turnover, and fine root turnover in soil. We also consider the NPP of understory vegetation or moss. These NPP-age relationships under different site conditions indicate that the NPP values of broadleaved and coniferous, as well as broadleaved mixed forests increase rapidly and reach a maximum when in young forests. However, for coniferous forest types, the maximum NPP generally occurs in mature forests. In addition, a higher SCI leads to a higher NPP value. Finally, we input these NPP-age relationships at various SCI values into the Integrated Terrestrial Ecosystem Carbon (InTEC) model to modify NPP modeling to estimate NPP in Heilongjiang Province in China from 2001 to 2010. All of the results showed that the methods reported in this study provide a reliable approach for estimating regional forest carbon budgets.
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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 | 0.002 |
| 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".