Changes in leaf functional traits of <i>Phoebe chekiangensis</i> underplanted in moso bamboo forests with different densities
Bibliographic record
Abstract
Leaf functional traits are sensitive to environmental changes and are a current hotspot in ecology. The objective of this study was to explore the changes in leaf functional traits of Phoebe chekiangensis underplanted in moso bamboo forests with different densities. Leaf functional traits of P. chekiangensis were determined, and their relationships were investigated. Results showed that leaf area (LA) and specific leaf area (SLA) increased with the increase of bamboo forest density, and they were significantly lower at 1350 individual·ha −1 than that of 2250 individual·ha −1 . However, leaf thickness (LT) and leaf mass per unit area (LMA) showed an opposite trend, and they were significantly higher at 1350 individual·ha −1 than that of 2250 individual·ha −1 . No significant difference was found in chlorophyll a (Ca), chlorophyll b (Cb), total chlorophyll (Ca + Cb), and total carotenoids (Car) among the three treatments. Ca/Cb showed a decreasing trend, and it was significantly higher at 1350 individual·ha −1 than that of 2250 individual·ha −1 . Leaf nitrogen (N) concentration increased with the increase of bamboo forest density, and it was significantly higher at 2250 individual·ha −1 than that of 1350 individual·ha −1 . C/N decreased, and it was significantly higher at 1350 individual·ha −1 than that of 2250 individual·ha −1 . LT was positively correlated with LMA, and negatively correlated with SLA. N concentration was positively correlated with SLA, and negatively correlated with LMA. Significant positive correlation between Ca + Cb and C concentration was observed. In conclusion, the leaf functional traits of P. chekiangensis could compensate for light deficiency through certain trait variations and combinations and better adapt to the environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".