Integrating Functional Genomics with Breeding in Eucommia ulmoides
Bibliographic record
Abstract
This involved constructing high-density genetic maps, analyzing quantitative trait loci (QTL) for growth traits, and identifying key genes involved in various biological processes. The study successfully constructed a high-density genetic map using single-nucleotide polymorphism (SNP) markers, covering 90% of the E. ulmoides genome with a total genetic distance of 4051.11 cM and an average marker distance of 0.45 cM. A total of 44 QTLs associated with growth traits were identified, along with 33 candidate genes related to energy storage, signal transmission, hormones, and metabolic pathways. Additionally, the genome of E. ulmoides was sequenced, revealing insights into sex differentiation and α-linolenic acid biosynthesis. The study also identified 71 NAC transcription factors and their potential role in rubber biosynthesis, and 119 MYB transcription factors involved in growth and development. The integration of functional genomics with breeding in Eucommia ulmoides has provided a solid foundation for future genetic improvement and breeding programs. The identification of key QTLs and candidate genes will facilitate targeted breeding strategies to enhance desirable traits, thereby improving the economic and ecological value of this important species.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".