GENETIC AND DEVELOPING GENOMIC RESOURCES IN BLACK RASPBERRY
Bibliographic record
Abstract
Over the last 75 years, the black raspberry industry in the United States has steadily declined due to lack of adapted and disease resistant cultivars. The high anthocyanin content of black raspberry and associated health benefits have revived interest in production and breeding new cultivars. The United States Department of Agriculture (USDA) Agricultural Research Service, National Clonal Germplasm Repository manages black raspberry germplasm and maintains a collection of over 175 accessions. Wild black raspberries collected in their native range from more than 130 locations across 27 US states and two Canadian provinces were recently added to this collection. Evaluation of this wild germplasm led to the identification of four sources of aphid resistance, two of which were introgressed into the elite breeding pool in two mapping populations. A major focus of this project is to develop, and make available, genomic tools including linkage and physical maps, a draft genome assembly, ESTs, SNP and SSR markers for use in black and red raspberry breeding. We will study genotype by environment interactions in this black raspberry germplasm in four different production regions across North America and apply the genomic tools to identify QTL important for breeding objectives. These tools will facilitate informed decisions regarding germplasm value and usage, crossing, and selection through marker-assisted breeding, and will be useful for breeding programs across the US. Here, we present the current status of global genetic resources and genomic research in black raspberry.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".