Genetic Basis of Tree Size and Fruit Yield in Durian Roles of Auxin and Cytokinin Signaling Pathways
Bibliographic record
Abstract
Durian ( Durio zibethinus ), widely known as the "King of Fruits," holds significant economic and cultural value across Southeast Asia. The size of its trees and fruit yield are critical agronomic traits, directly affecting cultivation efficiency and commercial viability. Despite their importance, the genetic underpinnings of these traits remain insufficiently understood. This study explores the influence of auxin and cytokinin signaling pathways in shaping durian tree architecture and determining fruit productivity. Auxin primarily regulates apical dominance and cell elongation, influencing overall tree morphology, while cytokinin drives branch differentiation and canopy expansion. During fruit development, auxin plays a crucial role in fruit set and expansion, whereas cytokinin modulates fruit number and size by controlling cell division rates. The balance between these two plant hormones is essential for optimizing durian growth and yield. Advancements in molecular breeding technologies, such as genetic modification and marker-assisted selection, present new opportunities for durian productivity enhancement. Understanding the intricate interactions between auxin and cytokinin at the genetic level will not only deepen our comprehension of durian growth and fruiting but also provide valuable insights for precision breeding and improved orchard management.
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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 | 0.001 |
| 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".