Using Living Germplasm Collections to Characterize, Improve, and Conserve Woody Perennials
Bibliographic record
Abstract
ABSTRACT Woody perennial plants make up nearly half of plant diversity and represent one‐third of the world's major crop species, yet effective strategies to maintain and preserve these important species require additional attention. The majority of conservation programs focus on seed storage; however, seeds of many woody perennial plants are difficult to maintain in seed banks because they are recalcitrant. In addition, most woody perennial crops are clonally propagated, and seed‐based conservation efforts miss clonal lineages that form the foundation of woody perennial agriculture. Woody crops are often best maintained as living collections, but these compose only 5.8% of ex situ germplasm collections. Living germplasm collections are critical resources for exploring and conserving genetic and phenotypic diversity and provide novel material for breeding efforts. In this review, we examine how living germplasm collections can be used for for phenotypic description, genetic characterization, and plant breeding. Lastly, we outline the importance of conserving these valuable resources and highlight the need for conservation strategies that are appropriately designed for woody perennial 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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".