Carbon storage, net primary production, and net ecosystem production in four major temperate forest types in northeastern China
Bibliographic record
Abstract
Temperate forests in northeastern China play a key role in the national carbon (C) budget; however, this role has been poorly quantified. The objective of this study was to quantify C storage, net primary production (NPP), and net ecosystem production (NEP) in four major temperate forest types in northeastern China. The four forest types include a primary mixed broadleaf – Korean pine (Pinus koraiensis Siebold & Zucc.) old-growth forest and three mid-aged regenerating forests, i.e., a secondary birch (Betula platyphylla Sukaczev) forest, a Korean pine plantation, and a Dahurian larch (Larix gmelinii (Rupr.) Rupr.) plantation. Total C storage differed significantly among the four forest types, with the highest storage (315.4 t C·ha −1 ) in the old-growth forest. Soil organic C accounted for 55%–70% of the ecosystem C, whereas vegetation C accounted for 28%–43% of the ecosystem C. Soil organic C storage in the two plantations was significantly lower than that in old-growth and secondary birch forests. The allocation (aboveground and belowground) of NPP, but not the total NPP, differed significantly among the forest types. Litterfall (44%–60%) and fine root production (43%–47%) contributed the largest proportion of the aboveground and belowground NPP, respectively. The highest NEP was in the Korean pine plantation (328.0 g C·m −2 ·year −1 ), followed by the old-growth (311.9 g C·m −2 ·year −1 ) and secondary birch (231.1 g C·m −2 ·year −1 ) forests, with the lowest NEP in the Dahurian larch plantation (187.9 g C·m −2 ·year −1 ). These results suggest that the major forest types are currently C sinks and Korean pine plantation establishment can be a promising approach for increasing C sequestration in northeastern China.
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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 | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".