Enhancement of phytolith-occluded carbon accumulation of Moso bamboo response to temperatures elevation and different fertilization
Bibliographic record
Abstract
The accumulation of phytolith-occluded carbon (PhytOC) in Moso bamboo could be a novel long-term carbon sequestration strategy. The objective of this study was to investigate the effects of temperature change and different fertilization on PhytOC accumulation. The pot experiment was established with different fertilization (including control (CK), nitrogen fertilizers (N), silicon fertilizers (Si), and a combination of nitrogen and silicon (NSi)) under high- and low-temperature. Despite the different fertilization, the PhytOC accumulation of the high-temperature group increases by 45.3% on average compared with the low-temperature group, suggesting higher temperature is greatly beneficial to the PhytOC accumulation. Fertilization significantly increases the accumulation of PhytOC (increased by 80.7% and 48.4% on average for the low- and high-temperature group, respectively) compared with CK. However, the N treatment increased both Moso bamboo biomass and PhytOC accumulation. The difference in the accumulation of PhytOC in Si and NSi was insignificant, indicating the combination of N and Si didn't bring extra benefit to PhytOC accumulation compared to Si fertilizer alone. These results indicated the application of nitrogen fertilizer is a practical and effective method for enhancing long-term carbon sequestration for Moso bamboo. Based on our study, we conclude that global warming poses a positive effect on promoting the long-term carbon sequestration of Moso bamboo.
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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".