Variation in growth, leaf, and wood property traits of Chinese white poplar (<i>Populus tomentosa</i>), a major industrial tree species in Northern China
Bibliographic record
Abstract
The natural phenotypic variation in Chinese white poplar (Populus tomentosa Carr.), which is distributed across a wide geographical area of northern China (30°N–40°N, 105°E–125°E), is a potential source of beneficial variation for poplar breeding. Thirteen traits related to growth, leaf, and wood properties were quantified in 460 P. tomentosa individuals grown in a common garden plot. There was considerable range-wide phenotypic variation in all traits across individuals according to the patterns of ANOVA among hierarchical groups (populations and regions, respectively). A clear sexual dimorphism for seven traits was examined. In total, 32 trait–trait phenotypic correlations (P ≤ 0.05), 10 trait–geographical factor correlations (P ≤ 0.05), and a highly interrelated structure network were identified, which was further supported by principal component analysis (PCA). These associations can be used in multiple-trait selective breeding programs for advantageous phenotypic traits. A hierarchical cluster analysis was used to classify four groups (southeastern, central, northeastern, and southwestern populations) among the natural populations using these 13 phenotypic traits. This study provides important perspectives into the use of direct breeding to potentially improve economic traits and provides a starting point for genome-wide association studies in P. tomentosa in the near future.
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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 | 0.002 | 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".