Sex-Related Differences of Ginkgo biloba in Growth Traits and Wood Properties
Bibliographic record
Abstract
Ginkgo biloba is one of the most widely cultivated dioecious timber trees in China. Understanding sex-related differences and how they affect growth traits and wood properties is crucial for informed management and optimal utilization of ginkgoes. In the present study, we collected 42 ginkgo samples and conducted DNA molecular identification to determine their sex. The result was a 1:1 ratio of male to female specimens. In addition, we measured 16 growth-trait and wood-property indices for these samples using advanced equipment, such as X-ray diffraction (XRD) and the Hitman ST300 standing tree tool. For growth traits, significant differences were observed between male and female ginkgoes in terms of the diameter at breast height (DBH), clear bole height (CBH), height, and volume. Significant differences were identified in wood properties between male and female ginkgoes in terms of the degree of cellulose crystallinity (DCC), cell length, cell wall thickness, and wall-to-lumen ratio. Tracheids from female trees were found to be wider, with thicker cell walls, than those from male trees. Principal component analysis (PCA) showed that there was a slight separation between the sexes in terms of all growth traits, whereas there was no separation in wood properties. The membership function value (MFV) also showed that male ginkgo exhibited a more robust phenotype than female ginkgo. The selection of male ginkgo for breeding and utilization offers distinct advantages for practical production.
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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 | 0.000 |
| 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) | 0.002 | 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".