USING GENERAL AND SPECIFIC COMBINING ABILITY TO FURTHER ADVANCE STRAWBERRY (FRAGARIA SP.) BREEDING
Bibliographic record
Abstract
Strawberry is one of the five fruit crops included in the USDA-funded multi-institutional and trans-disciplinary project, "RosBREED: Enabling Marker-Assisted Breeding in Rosaceae". A Crop Reference Set (CRS) was developed of 900 genotypes and seedlings from 40 crosses representing the breadth of relevant diversity and encompassing founders used in breeding the domesticated strawberry. Individual native species and cultivar genotypes were included along with 10 progeny from 36 of the crosses of genotypes representing eastern and western North American and European short day and remontant cultivars. This CRS has been phenotyped in five U.S. states. Over 14 fruit quality traits have been studied, as well as remontancy, truss size, peduncle length, crop estimate, plant architecture, and disease resistance. The phenotyping conducted in the first growing season showed considerable variability amongst the genotypes and the locations for all of the characteristics. General and specific combining ability variance components were determined from the populations in order to provide breeders with guidance on the most effective breeding strategies for incorporating the superior traits from this germplasm into their programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".