The Seasonal Impact of Thinning Intensities on Soil Carbon Cycling in the Lesser Xing’an Range, Northeast China
Bibliographic record
Abstract
Forest degradation, driven by human and natural factors, diminishes ecological functions and carbon storage. Understanding the complex dynamics of soil carbon pools is crucial for the global carbon cycle, although these dynamics are poorly understood. This study examines how different thinning intensities influence seasonal soil carbon cycling in degraded forests. ANOVA revealed significant differences in soil properties across treatments (p < 0.05). Redundancy analysis and random forest analyses were used to explore relationships among thinning intensities, soil properties, and carbon sequestration. Thinning significantly altered soil attributes, as revealed by field experiments and data analysis. Moderate thinning (20% intensity) significantly enhanced litter retention and soil nutrient levels year-round (p < 0.05). Seasonal variations affected soil carbon dynamics and lower thinning intensities improved carbon sequestration in spring and summer. Conversely, higher thinning intensities led to carbon loss in autumn and winter. Litter carbon, fine root carbon, and correction factor significantly respond to thinning intensities year-round as examined through redundancy analysis and random forest analyses. Findings indicate moderate thinning effectively enhances soil carbon sequestration in degraded forests. Strategically planned thinning could aid climate change mitigation by boosting forest soil carbon storage, influencing forest management and conservation.
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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 | 0.001 | 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 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".