Individual size but not additional nitrogen regulates tree carbon sequestration in a subtropical forest
Bibliographic record
Abstract
Abstract Recent studies have indicated that tree carbon accumulation in subtropical forests has been negatively affected by global change phenomena such as warming and drought. However, the long-term effect of nitrogen addition on plant carbon storage remains poorly understood in these regions. In this study, we conducted a 10-year field experiment examining the effect of experimental N addition on plant growth and carbon storage in a subtropical Chinese fir forest. The N levels were 0 (control), 60, 120, and 240 kg ha−1yr−1, and the N effects on tree carbon were divided into stand and individual levels. The results indicated that tree carbon storage at the stand scale was not affected by long-term N addition in the subtropical forest. By contrast, significant impacts of different tree size classes on carbon sequestration were found under different N treatments, which indicated that the amount of plant carbon sequestration was significantly enhanced with tree size class. Our findings highlight the importance of community structure and growth characteristics in Chinese fir forests, in which individual size but not additional N regulates tree carbon sequestration in this subtropical forest.
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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 | 0.000 | 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) | 0.001 | 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".