Genetic Diversity and Relationship in Squash Using Morphological, Chemical and Molecular Analyses
Bibliographic record
Abstract
Germplasm characterization is an important link between the conservation and utilization of plant genetic resources. A collection of thirteen summer, spaghetti and acorn squash ( Cucurbita pepo L .) and one winter squash ( Cucurbita moschata L .) germplasm accessions were screened using morphological (leaf area, fruit weight, fruit length, fruit firmness), chemical [Soluble Solids Content (S.S.C.), ascorbic acid] and molecular [Random Amplified Polymorphic DNA (RAPD)] analyses. A wide range of variability among genotypes was recorded for morphological and chemical characteristics. The percentages of variation were 217%, 532%, 175%, 166%, 148%, 520% for leaf area, fruit length, fruit weight, fruit firmness, S.S.C, ascorbic acid content, respectively. Both significant positive and negative correlations have been found between the morphological and chemical characteristics. The RAPD analysis produced 209 DNA fragments with 100% polymorphism in two or more squash genotypes while none of the fragments showed monomorphic behavior among squash genotypes. At a similarity level of 82% the genotypes were divided into two clusters. The first cluster consisted of eight genotypes (PI 506466- PI 292014- PI 518688- PI 615119- PI 136448- Butternut- Copi- Eskandrani). The second cluster contained only two genotypes (Yellow Crookneck and Shamamy). When the cluster analysis of RAPD patterns was associated with morphological and chemical evaluation of squash genotypes used in this study, there was a notable degree of agreement. The RAPD-PCR was found to be suitable for use with squash due to its ability to discriminate between genotypes and determine their genetic relationship. Results obtained in this investigation provide clear evidence that there is a considerable variation among summer squash genotypes. Present results support the development of breeding programs in squash since high genetic variability in its accessions and landraces has been found.
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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) | 0.001 | 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".