Discrimination and assessment of black walnut (<i>Juglans nigra</i> L.) cultivars using phenology and microsatellite markers (SSRs)
Bibliographic record
Abstract
Black walnut (Juglans nigra L.), a large tree native throughout the eastern United States, produces a high-quality edible nut. Our goal was to maintain the integrity of black walnut breeding programs by verifying the identity of accessions. We sampled 285 ramets of 78 cultivars from the black walnut nut breeding orchards and clonal repositories at the University of Missouri and Kansas State University. We employed both phenotypic and genotypic methods to identify and differentiate cultivars. Phenotypes were evaluated using seven phenological traits. Cultivars varied for all traits among each of the 4 yr, but the best morphological characteristics for evaluating cultivar identity were bud break date and date of first pistillate bloom. Samples (n = 285) were genotyped using 10 polymorphic microsatellite loci. The simple sequence repeats produced a total of 174 alleles and 17.2 alleles per locus. We detected 47 unique genotypes represented by more than one sample, including 128 instances of identical genotypes with different names (synonyms) and 106 instances of different genotypes with a shared name (homonyms). Our results indicated that multiple errors were committed during the propagation of these important cultivars. It may be difficult to determine which genotype is original to a cultivar name in the absence of a foundation plant materials collection or vouchered specimens. These results will assist black walnut breeders and producers by improving the integrity of breeding collections and by identifying the best phenological traits for rapid assessment of trueness to type.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".