Analysis of phylogenetic relationship among five mulberry (<i>Morus</i>) species using molecular markers
Bibliographic record
Abstract
Species identification in mulberry (Morus) continues to be a point of great debate among scientists despite the number of criteria such as floral characters, wood, and leaf anatomical and biochemical characters used to identify the species within this genus. However, no consensus system of classification has emerged. Hence, an investigation was undertaken with inter-simple sequence repeat (ISSR) and random amplified polymorphic DNA (RAPD) markers to find out the possibility of using these DNA markers to confirm the identity of genotypes in a particular species. Fifteen ISSR and 15 RAPD primers generated 86% and 78% polymorphism, respectively, among 19 mulberry genotypes. The polymorphism among the species varied from 50% to 57% in ISSR markers and 31% to 53% in RAPD markers. Similarity coefficients were higher among the genotypes of M. latifolia, M. bombycis and M. alba. Cluster analyses separated genotypes of M. laevigata and M. indica from those of the other species. Population structure analysis of these species further showed high genetic differentiation coefficients (GST), high heterozygosity between two species (DST), and total heterozygosity among populations (Ht) coupled with considerably low gene flow (Nm) when M. laevigata was paired with other species. Based on these parameters and the result of cluster analysis it is concluded that M. laevigata can be considered as a separate species of mulberry, whereas the other four species may be grouped together and treated as subspecies.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".