Breeding Trait Priorities of the Blueberry Industry in the United States and Canada
Bibliographic record
Abstract
Developing new blueberry cultivars requires plant breeders to be aware of current and emerging needs throughout the supply chain, from producer to consumer. Because breeding perennial crop plants (such as blueberry) is time- and resource-intensive, understanding and targeting priority traits is critical to enhancing the efficiency of breeding programs. This study assesses blueberry industry breeding priorities for fruit and plant quality traits based on a survey conducted at commodity group meetings across nine U.S. states and in British Columbia (Canada) between Nov. 2016 and Mar. 2017. In general, industry responses signaled that the most important trait cluster was fruit quality including the firmness, flavor, and shelf life. Fruit quality traits affect price premiums received by producers; influence consumer’s preferences; and have the potential to increase the feasibility of mechanical harvesting, all critical to the economic viability of the industry. There were differences across regions in the relative importance assigned to traits for disease resistance, arthropod resistance, and tolerance to abiotic stresses. Our findings will be useful to researchers seeking solutions for challenges to the North American blueberry industry including development of new cultivars with improved traits using accelerated DNA-based selection strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".