Intensive Management Increases Phytolith-Occluded Carbon Sequestration in Moso Bamboo Plantations in Subtropical China
Bibliographic record
Abstract
Plantation management practices could markedly change the sequestration of phytolith-occluded carbon (PhytOC) in plants and soils. However, for Moso bamboo (Phyllostachys pubescens) plantations, the effect of intensive plantation management (including fertilization, tillage, and removal of understory vegetation) on the accretion rate of PhytOC in the soil-plant system is much less understood than extensive management (without fertilization, tillage, and removal of understory vegetation). The objectives of this study were to investigate the effect of intensive and extensive management practices on the production, accumulation, and runoff of PhytOC and their distribution in physical fractions in Moso bamboo plantations. Our results showed that intensive management (1) increased PhytOC production mainly due to increased forest productivity; (2) increased PhytOC storage in the heavy fraction but decreased its storage in the light fraction of organic matter, resulting in the lack of effect on soil PhytOC storage; (3) increased the rate of dissolution of phytolith and the loss of PhytOC in runoff; and (4) promoted PhytOC sequestration in the soil-plant system, mostly in the plants, due to the greater rate of PhytOC production than the rate of loss. We conclude that intensive bamboo plantation management practices are beneficial to increasing long-term PhytOC sequestration in the soil-plant system.
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 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 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".