Genetic and genomic resources for Rubus breeding: a roadmap for the future
Bibliographic record
Abstract
Abstract Rubusfruits are high-value crops that are sought after by consumers for their flavor, visual appeal, and health benefits. To meet this demand, production of red and black raspberries (R. idaeusL. andR. occidentalisL.), blackberries (R. subgenusRubus), and hybrids, such as Boysenberry and marionberry, is growing worldwide.Rubusbreeding programmes are continually striving to improve flavor, texture, machine harvestability, and yield, provide pest and disease resistance, improve storage and processing properties, and optimize fruits and plants for different production and harvest systems. Breeders face numerous challenges, such as polyploidy, the lack of genetic diversity in many of the elite cultivars, and until recently, the relative shortage of genetic and genomic resources available forRubus. This review will highlight the development of continually improving genetic maps, the identification of Quantitative Trait Loci (QTL)s controlling key traits, draft genomes for red and black raspberry, and efforts to improve gene models. The development of genetic maps and markers, the molecular characterization of wild species and germplasm, and high-throughput genotyping platforms will expedite breeding of improved cultivars. Fully sequenced genomes and accurate gene models facilitate identification of genes underlying traits of interest and enable gene editing technologies such as CRISPR/Cas9.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".