Breeding Trait Priorities of the Cranberry Industry in the United States and Canada
Bibliographic record
Abstract
Informed assessment of priority genetic traits in plant breeding programs is important to improve the efficiency of developing cultivars suited to current climate and industry needs. The efficiency of genetic improvement is critical for perennial crops such as cranberries, as they usually involve more resources, time, and funding compared with other crops. This study investigated the relative importance of cranberry producers’ preferences for breeding traits related to fruit quality, productivity, plant physiology, and resistance to biotic and abiotic stresses. Industry responses revealed that fruit characteristics affecting fruit quality, including firmness, fruit size and anthocyanin content, and resistance to fruit rot, were the most desired traits in new cranberry cultivar release. These traits have the potential to increase the quality standards needed to process high-value sweetened dried cranberry products, positively affecting price premiums received by producers, which is critical for the economic viability of the cranberry industry. Our findings will be useful to breeders and allied scientists seeking to develop an advanced DNA-based selection strategy that would impact the global cranberry industry.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".