Genetic relatedness among cultivated and wild mulberry (Moraceae: Morus) as revealed by inter-simple sequence repeat analysis in China
Bibliographic record
Abstract
The genetic diversity of 27 mulberry (Morus spp.) genotypes mainly from China was investigated using inter-simple sequence repeat (ISSR) markers to assist in addressing breeding objectives and conserving existing genetic resources. Of the 22 primers screened, 15 produced highly reproducible ISSR bands. Using these 15 primers, 138 discernible DNA fragments were generated with 126 (91.3%) being polymorphic, indicating considerable genetic variation among the mulberry genotypes studied. Genetic similarity ranged from 0.6014 between Yu 2 and Yu 711 to 0.9493 between Cuizhisang and Dejiang 10. The phenetic dendrogram based on ISSR data generated by the unweighed pair group method with arithmetical averages (UPGMA) method grouped the 27 accessions into two major clusters: cluster I, cultivated mulberry species (M. multicaulis Perr., M. alba Linn., M. atropurpurea oxb., M. bombycis Kiodz., M. australis Poir., M. rotundiloba Kiodz., M. alba var. pendula Dipp., M. alba var. macrophylla Loud., and M. alba var. venose Delile.); and cluster II, wild mulberry species (M. cathayana Hemsl., M. laevigata Wall., M. wittiorum Hand-Mazz., M. nigra Linn., and M. mongolica Schneid.). Our molecular analyses agree with the existing morphological classification of Morus and clarify the genetic relationships among mulberry species. Key words: Morus L., genetic diversity, inter-simple sequence repeat, relatedness
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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 | 0.000 |
| 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 | 0.000 |
| 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".