From Wild Type to Premium Cultivars: Selective Evolution of Key Trait Genes in Durian Domestication
Bibliographic record
Abstract
This study talks about the process of durian changing from wild state to the high-quality variety we eat now. The key point is how some key genes behind these changes are selected step by step by people. The study used several methods, such as population genome analysis, whole genome association analysis, and some molecular experiments. Together, these methods help us see a problem: how humans affect some important traits of the fruit, such as taste, disease resistance and yield, when selecting durian. The study also mentioned that these changes in durian cannot be explained by a single gene. There is a complex genetic structure behind them. Some are controlled by one gene, and some may be determined by many genes together. Some are “hard selection” and some are “soft selection”, and the traces they leave on the genome are also different. In addition, newly emerged mutations and existing gene mutations often work together. In addition to artificial selection, environmental changes and consumer taste preferences have also promoted the increasing diversification of durian varieties. This study not only allows us to better understand the genetic background of durian, but also provides important ideas and genetic resources for the future improvement of durian and other fruits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".