New Varieties of Blueberry Released by US in 2018 and Analysis of Breeding Trends
Bibliographic record
Abstract
In 2018, the United States Department of Agriculture (USDA-ARS), the Clemson University, and the University of California jointly announced 40 new varieties of blueberry, including 12 varieties of northern highbush blueberry, 21 varieties of southern highbush blueberry, and 7 varieties of ornamental blueberry. Based on the analysis of the comprehensive characteristics of the announced blueberry varieties, this paper summarizes the current development trend of global blueberry breeding. The results have been shown that: 1) the cultivation of southern highbush blueberry is still the main direction of blueberry breeding, and the number of new ornamental blueberry varieties has increased. 2) The main breeding direction for northern highbush blueberry is to cultivate new varieties with early maturity, large fruit, hard texture, and good storability. 3) The breeding trend of blueberries in the southern highbush blueberry is mainly focused on cultivating new cultivars with the low chilling requirement and have comprehensive characteristics such as early maturity, large fruit, and good fruit quality. 4) The main direction of ornamental blueberry breeding is to pay attention to the diversification of fruit color and the leaf color that changes with the season for use in Garden potted plants and landscaping. 5) In recent years, China has made rapid progress in blueberry breeding except for the traditional breeding countries such as Europe and America. The breeding trend described in this paper will point out the direction for blueberry breeding in China in the future and have important practical reference value.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".