Taming the wild: domesticating untapped northern fruit tree and shrub resources in the era of high-throughput technologies
Bibliographic record
Abstract
Abstract New crop`s need to emerge to provide sustainable solutions to climate change and increasing abiotic and biotic constraints on agriculture. A large breadth of northern fruit trees and shrubs exhibit a high potential for domestication; however, obstacles to implementing traditional breeding methods have hampered or dissuaded efforts for improvement. This review article proposes a unique roadmap for de novo domestication of northern fruit crops, with a focus on biotechnological (e.g. genome editing, rapid cycle breeding, and in planta transformation) approaches that can boast rapid evolutionary gains. In addition, numerous biotechnological (e.g. virus-induced flowering and grafting-mediated flowering) and breeding strategies (e.g. adaptation of speed breeding to fruit trees) that can hasten the transition from juvenility to sexual maturity are described. A description of an accelerated genetic breeding strategy with insights for 16 underutilized species (e.g. shagbark hickory, running serviceberry, horse chestnut, and black walnut) is provided to support their enhancement. Deemed unrealistic only a decade ago, progress in the realm of bioengineering heralds a future for northern orphan crops through the implementation of fast-tracked crop improvement programs. As such, the roadmap presented in this article paves the way to integrating these novel biotechnological discoveries and propel the development of these forgotten crops in a sustainable and timely manner.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".