Genomic Advances in Cucurbitaceae: Implications for Crop Improvement and Breeding
Bibliographic record
Abstract
The Cucurbitaceae family, encompassing a wide array of economically and nutritionally significant crops, has been the focus of extensive genomic research aimed at enhancing breeding and crop improvement.Recent advancements in sequencing technologies and bioinformatics have led to the sequencing of genomes from various Cucurbitaceae species, providing valuable insights into gene identification, genome evolution, and genetic variation.This has opened new avenues for molecular breeding, leveraging genetic transformation and gene editing technologies, including CRISPR/Cas9, to overcome the limitations of conventional breeding methods.The integration of next-generation sequencing (NGS) and omics approaches has furthered our understanding of complex traits, such as disease resistance and fruit quality, and has facilitated the development of high-density genetic maps and the identification of functional genes.Additionally, the construction of genetic and cytogenetic maps has been instrumental in revealing the genomic structure of cucurbit crops, aiding in the alignment of linkage groups with chromosomes and enhancing marker-assisted selection.The exploration of genetic diversity through the analysis of wild Cucurbitaceae species using cytogenetic mapping has also contributed to the phylogenetic understanding and breeding resource development.With the accumulation of genomic resources and the advent of high-throughput genotyping methods, new strategies such as genome-wide association studies (GWAS) and the use of multi-parent populations have emerged, leading to the discovery of quantitative trait loci (QTL) for key agronomic traits.The synergy of these genomic tools and their implications for breeding is poised to revolutionize the improvement of Cucurbitaceae crops, ensuring food security and meeting the demands of a growing population.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".