Global insights into the effects of forest thinning on soil, microbial, and enzyme C–N–P stoichiometry and microbial nutrient limitation
Bibliographic record
Abstract
Thinning plays an important role in regulating stand density and improving interspecific relationships. In this study, we examined the effects of thinning on soil, microbial, and enzyme C–N–P stoichiometry by integrating 1186 pairwise observations in different forest types (coniferous, broadleaf, and mixed), recovery times (<5, 5–10, and >10 years), thinning intensities (light, moderate, and heavy), and relative humidity indices (integrate the combined effects of background climate: <30, 30–50, and >50). Thinning significantly increased the C:P ratio in the soil (4.3%), microbial (10.8%), and enzyme (5.3%), and the N:P ratio in the soil (3.6%) and enzyme (12.8%). However, thinning decreased the C:N ratio in microbial (5.3%) and enzyme (16.3%) and the vector angle (1.2%). Thinning mainly affected the microbial C:N ratio in coniferous and mixed forests. The soil C:N, microbial C:P, and N:P ratios decreased, whereas the vector angle increased with recovery time. The enzyme C:N ratio decreased, whereas the enzyme N:P ratio increased with thinning intensity. The soil N:P ratio, enzyme N:P ratio, and vector angle increased with increasing relative humidity index. The results highlighted that the soil nutrient cycling process, microbial activity, and C–N–P stoichiometry were significantly affected by thinning. Recovery time, thinning intensity, and background climate were important factors regulating these changes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".