Morphological Characterization of Mulberry (Morus spp.) Accessions Grown in Kenya
Why this work is in the frame
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Bibliographic record
Abstract
<p>Genetic divergence of five mulberry accessions including Embu, Thika, Thailand (<em>M. alba</em>), Kanva-2 and S41 (<em>M. indica</em>) grown in Kenya were examined using twelve phenotypic traits. The assessment of phenotypic traits was done in a field study in two localities, Nairobi and Eldoret. The traits that were significantly different across the mulberry accessions included lamina width and petiole length (P ? 0.01), petiole width and growth height (P ? 0.05), internodes distance and number of branches (P ? 0.001). The Duncan’s Multiple Range Test (DMRT) results were used to generate a dendrogram derived from hierarchical cluster analysis that further partitioned the mulberry accessions into four groups. Embu and Thailand accessions grouped together while S41, Thika and Kanva-2 accessions grouped separately. Embu and Thailand accessions were characterized by fewer numbers of branches than the rest of the accessions. Thika accession had high number of branches and short internode distance. Significant and positive correlations were found between leaf yield traits except in internode distance and number of branches which were significant and negatively correlated. Significant and positive correlations can be utilized since they are rewarding for mulberry leaf yield improvement.</p>
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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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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