An Investigation of Trait Prioritization in Rosaceous Fruit Breeding Programs
Bibliographic record
Abstract
Over 60 rosaceous crop breeding programs exist in North America, but no information has been available on which traits are targeted for selection or how breeders make such decisions. We surveyed all active rosaceous fruit breeding programs in the United States and Canada to determine: 1) the relative importance of over 50 plant traits that breeders select for 2) the likelihood of selection for the most important traits; and 3) the factors influencing breeders’ decisions. A double-bounded Tobit model was used to investigate the effect of supply chain parties, technical and socioeconomic challenges, and crop characteristics on the likelihood of selection for trait clusters. We found that consumer-driven forces positively impact the likelihood of selection for traits more than producer forces and a breeder’s own experience. Technical factors are as important as socioeconomic factors but less important than market-related factors. Our findings provide the first ever evidence that a socioeconomic approach in specialty crop breeding programs can contribute to an improved understanding of the effects of different supply chain factors on breeding programs’ trait priority setting.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".