Classification Framework of Introduced Crabapple (Malus spp.) Cultivars Based on Morphological and Numerical Traits: Insights for Germplasm Conservation and Landscape Forestry
Bibliographic record
Abstract
Crabapples (Malus spp.) are widely planted ornamental and multipurpose trees in temperate regions and represent an important component of forest and landscape resources. However, the absence of a standardized classification framework has led to nomenclatural confusion, hindering germplasm conservation, breeding, and international exchange. In this study, 80 introduced crabapple cultivars preserved in the germplasm repository of Nanjing Forestry University were systematically evaluated using 55 morphological traits of flowers, leaves, fruits, and tree architecture. A hierarchical framework was established based on flower type and corolla color, dividing cultivars into Single, Semidouble, and Double Flower groups, with further subdivisions of Single cultivars by color. Numerical taxonomy (R- and Q-type clustering) validated the robustness of this framework, identifying petal number and corolla color as the most consistent traits across cultivars and seasons (inter-cultivar CV < 10%), serving as reliable diagnostic indicators, although within-cultivar variation was not quantified. The proposed system resolved frequent misidentifications (e.g., M. ‘Kelsey’ and M. ‘Molten Lava’) and provided standardized descriptors for cultivar identification. Beyond taxonomy, the framework enhances germplasm management, supports nursery production and landscape forestry, and facilitates international exchange of ornamental resources. These findings highlight the potential of integrating morphological and numerical approaches for germplasm diversity assessment and contribute to the development of a unified global classification system for ornamental crabapples.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".