A compartmentation approach to deconstruct ecosystem carbon fluxes of a Moso bamboo forest in subtropical China
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 t C·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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".