A compartmentation approach to deconstruct ecosystem carbon fluxes of a Moso bamboo forest in subtropical China
Why this work is in the frame
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Bibliographic record
Abstract
Moso bamboo ( Phyllostachys edulis ) forests are a vital resource in subtropical China, known for their high carbon (C) sequestration capacity. However, the dynamic processes of C fluxes within each component (canopy, culm, and soil) and their individual contributions, particularly during on- and off-years, remain unclear. A 2-year field experiment was conducted to investigate the dynamics of C fluxes from the canopy, culm, and soil (partitioned into heterotrophic, rhizome, and stump respiration) and their contributions to net ecosystem productivity (NEP) in a representative Moso bamboo forest in the subtropical region of China. The average annual NEP of the Moso bamboo forest was 7.31 ± 2.76 t C·ha –1 . Specifically, the canopy’s annual net C uptake was 17.30 ± 3.23 tC·ha –1 , accounting for 237% of NEP. In contrast, C emissions from heterotrophs, culms, rhizomes, and stumps were 5.37 ± 1.20, 2.18 ± 1.05, 1.29 ± 0.04, and 1.15 ± 0.33 t C·ha –1 , accounting for −73%, −30%, −18%, and −16% of NEP, respectively. The NEP, net cumulative C uptake in the canopy, and C emissions from the respiration of heterotrophs and stumps were all significantly higher during on-years when compared to off-years, whereas C emissions from bamboo culms displayed opposite trends. These findings offer a new approach for quantifying the C budgets of Moso bamboo forests and provide valuable insights into the C cycling processes in forest ecosystems. • Contribution of each component of forest ecosystem to NEP was observed for the first time. • Average annual NEP of the Moso bamboo forest was 7.31 ± 2.76 t C ha -1 . • Canopy and culm contributed 237% and -30% to NEP, respectively. • Heterotrophic, rhizome and stump contributed -73%, -18% and -16%, respectively. • The NEP, canopy C uptake and soil C emissions were higher in on-year than off-year.
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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 | 0.000 | 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) | 0.000 | 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 it