Vertical stratification of biodiversity and productivity relationships and their genesis in South Subtropical Evergreen Broad-leaved Forest
Bibliographic record
Abstract
Exploring how biodiversity and productivity are related in different vertical layers of forests, and understanding the role of various biotic and abiotic factors, can shed light on the heterogeneous distribution of forest productivity and guide sustainable forest management. Based on the South Subtropical Evergreen Broad-leaved Forest, we divided woody plants into understory and overstory based on individual diameter at breast height and found that (1) the relationship between biodiversity and forest productivity varied among the understory, overstory, and whole community. The absolute value of the correlation coefficient between biodiversity and forest productivity tended to decrease from the understory to the overstory. (2) Soil organic carbon (SOC), species mingling index, and altitude were the dominant factors affecting understory productivity. Soil pH, uniform angle index (reflecting the spatial distribution of trees), and SOC were the dominant factors affecting overstory productivity. Soil pH, altitude, and SOC were the dominant factors affecting the whole productivity. (3) In the understory, only the soil factors and species diversity can directly and significantly affect understory productivity. In the overstory, soil factors, species diversity, and structural diversity can contribute significantly to overstory productivity directly. In the whole community, only the soil factors and topographic factors can directly and significantly affect whole productivity. Phylogenetic diversity is the primary factor affecting understory productivity, and soil attributes is the primary factor affecting overstory and whole productivity.
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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 | 0.001 |
| 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) | 0.001 | 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".