Differences in fine root traits between early and late-successional tree species in a Chinese subtropical forest
Bibliographic record
Abstract
The aim of this study was to compare fine root (≤2 mm diameter) traits (i.e. biomass distribution and architecture) of three tree species (Alniphyllum fortunei, Liquidambar formosana and Cyclobalanopsis glauca) growing in a mixed-species stand in a subtropical forest. Fine root samples were collected using soil cores. The collected samples were scanned with the Win-RHIZO system to analyse architectural parameters and were then oven-dried to determine dry mass. Fine roots of the three species were mainly distributed in the top 15 cm of soil and decreased with soil depth across all horizontal distances. C. glauca had the highest fine root biomass at 15–30 cm depth at 1.0 m from the tree trunk. The specific fine root area (SRA) and the length (SRL) were the highest for C. glauca, followed by L. formosana and A. fortunei. These species use different soil exploitation strategies. The early-successional species (A. fortunei and L. formosana) increase their fine root biomass and length through high carbon investment, whereas late-successional species (C. glauca) increase nutrient uptake efficiency via changes in fine root morphology and higher SRA and SRL values. In secondary broadleaved forest management and mixed plantation establishment, root trait differences among tree species and their effects on belowground competition and species coexistence should be considered.
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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".