Carbon Stock and Environmental Variations of Typical Plantations in Mufu Mountain in Hubei Province, China
Bibliographic record
Abstract
ABSTRACT Forest ecosystems play a critical role in the global carbon cycle. As a significant terrestrial carbon sink, plantations exhibit carbon stock patterns that are shaped by tree species composition, stand structure, and environmental conditions. Here, we investigated typical plantation types in the Mufu Mountain, Hubei Province. Total carbon stock and its distribution across different stand types were quantified by establishing permanent monitoring plots and conducting tree surveys, applying general biomass models to estimate biomass, and employing elemental analysis to measure soil carbon content. Our results indicated that total carbon stock ranged from 37,452.54 to 184,909.38 kg/ha among six forest subplots in the Mufu Mountain. Broadleaf and coniferous stands accumulated substantially more carbon than Phyllostachys edulis (Carrière) J. Houz. forests. Higher soil temperature, illuminance, and increased shrub cover promoted carbon accumulation in trees and shrubs. In contrast, multiple environmental factors regulated carbon stock in herbaceous plants, litter, and soil organic matter, demonstrating clear carbon pool‐specific effects. Our findings clarify key environmental drivers of carbon dynamics in subtropical plantations, and based on these results, we propose concrete management strategies including the selection of high‐carbon stock tree species, maintenance of understory shrub layers, and implementation of strategic canopy thinning to enhance forest carbon sequestration.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".