Genomics: A potential panacea for the perennial problem
Bibliographic record
Abstract
Perennial crops represent important fresh and processed food sources worldwide, but advancements in breeding perennials are often impeded due to their very nature. The perennial crops we rely on most for food take several years to reach production maturity and require large spaces to grow, which make breeding new cultivars costly compared with most annual crops. Because breeding perennials is inefficient and expensive, they are often grown in monocultures consisting of small numbers of elite cultivars that are vegetatively propagated for decades or even centuries. This practice puts many perennial crops at risk for calamity since they remain stationary in the face of evolving pest and disease pressures. Although there is tremendous genetic diversity available to them, perennial crop breeders often struggle to generate commercially successful cultivars in a timely and cost-effective manner because of the high costs of breeding. Moreover, consumers often expect the same cultivars to be available indefinitely, and there is often little or no incentive for growers and retailers to take the risk of adopting new cultivars. While genomics studies linking DNA variants to commercially important traits have been performed in diverse perennial crops, the translation of these studies into accelerated breeding of improved cultivars has been limited. Here we explain the "perennial problem" in detail and demonstrate how modern genomics tools can significantly improve the cost effectiveness of breeding perennial crops and thereby prevent crucial food sources from succumbing to the perils of perpetual propagation.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".