Discrimination and assessment of black walnut (<i>Juglans nigra</i> L.) cultivars using phenology and microsatellite markers (SSRs)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.001 | 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 it