Development of KASP fingerprinting panel for clonal identification in red raspberry (<scp><i>Rubus idaeus</i></scp> L.)
Bibliographic record
Abstract
Abstract Red raspberry is an economically important horticultural crop that is known for its fruit's sweet flavour and nutritional value. A reliable and economic genotyping platform is needed to facilitate clonal/variety identification. Previous attempts for clonal identification utilized morphological traits or low‐throughput, difficult to score dinucleotide‐containing simple sequence repeat molecular markers. Single nucleotide polymorphisms (SNPs), despite having lower allelic diversity, are numerous across the genome and more easily converted to high‐throughput assays restoring differential power. In this study, we use the kompetitive allele‐specific PCR (KASP™) chemistry, an affordable and high‐throughput platform, to develop a panel of SNPs to distinguish a diverse collection of red raspberry accessions for clonal identification. The panel consists of 48 KASP assays that show high concordance with whole genome sequencing, allelic balance, and recovery rate and a minimal set of 24 assays that distinguished the same accessions differentiated by the larger panel.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".