Breeding Trait Priorities of the Blueberry Industry in the United States and Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it