Genomic resources for crop wild relatives are critical for perennial fruit breeding and conservation
Bibliographic record
Abstract
Many perennial fruit crops are clonally propagated, resulting in uniform fruit quality but increasing vulnerability to pests, diseases, and climate change. In contrast, closely related crop wild relatives (CWRs) continue to evolve in response to these pressures and are a valuable source of adaptive traits. Despite their potential, CWRs are underutilized in perennial fruit breeding. Efficient and accurate introgression of traits from CWRs during perennial fruit breeding will require the use of genomics. Genomics-assisted breeding begins with genetic mapping, such as genome-wide association studies, to identify markers predictive of traits of interest. For diverse species such as CWRs, a pangenomic approach that incorporates multiple species as a reference is often necessary. Continued use of CWRs in fruit breeding also depends on their conservation, both in situ (in natural habitats) and ex situ (off-site). Ex situ collections can also be used for genetic mapping, further supporting genomics-assisted plant breeding efforts. Ultimately, breeding and conservation of perennial fruit crops are complementary goals that benefit from the development and application of genomic resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".