Detailed analysis of <i>Cucurbita pepo</i> seed coat types and structures with scanning electron microscopy
Bibliographic record
Abstract
Discovery of a mutant thin-coated seed phenotype at the end of the 19th century facilitated pumpkin (Cucurbita pepo L.) seed oil production and increased botanic interest in seed coat types and their structures. The main seed coat characteristics were usually analyzed by light and fluorescent microscopy, and more recently, seed coat traits have also been mapped on a C. pepo gene map. The aim of our research was to collect and describe various pumpkin seed types and to analyze, using scanning electron microscopy (SEM), the detailed structure of their seed coats. Seeds of 29 cultivars and landraces were collected and visually evaluated based on seed coat characteristics. Seed samples belonging to different seed types discovered in our collection were transversely sectioned and analyzed by SEM. Twelve seed types were determined, and SEM analysis revealed high variability in their seed coat structures. Using SEM, tissue and cell structures were clearly visible, and novel details of cell and tissue topography were documented. Hypodermal and aerenchyma cells in wild-type seed coats showed fibrous or reticulate secondary cell wall thickening, respectively. In mutant seed types, an absence of different seed coat layers was clearly noted, while the remaining layers were distinctly pronounced. A new completely hull-less seed type was described for the first time. Description of the variability of seed coats in pumpkin was complemented by novel seed coat types, and their structures were analyzed in detail the first time by SEM.
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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 | 0.000 | 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".