Genetic Analysis and Discrimination of Sweet Cherry Cultivars and Selections Using Amplified Fragment Length Polymorphism Fingerprints
Bibliographic record
Abstract
Amplified fragment length polymorphisms (AFLPs) were used to analyze the relationships between sweet cherry ( Prunus avium L.) cultivars and selections from the breeding program at the Pacific Agri-Food Research Centre in Summerland, Canada. Six pairs of preselected primers were used for the analysis of a total of 67 cultivars and selections. Scoring the absence and presence of 118 polymorphic DNA fragments produced a unique binary code for each cultivar and selection. Two phylogenetic trees were constructed using these 118 polymorphic fragments, one tree for 55 related cultivars and selections from the Summerland breeding program and the other for 23 self-incompatible cultivars of differing origins. The reliability of AFLP DNA fingerprints was confirmed by correlating relationships revealed by AFLP profiles with known genetic relationships of some sweet cherry cultivars and by a blind test for cultivar identification. Results indicate that AFLP analysis is a good technique to evaluate genetic distance and relationships in a sweet cherry breeding population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".