Data from: Latent developmental and evolutionary shapes embedded within the grapevine leaf
Bibliographic record
Abstract
Across plants, leaves exhibit profound diversity in shape. As a single leaf expands, its shape is in constant flux. Plants may also produce leaves with different shapes at successive nodes. In addition, leaf shape varies among individuals, populations and species as a result of evolutionary processes and environmental influences. Because leaf shape can vary in many different ways, theoretically, the effects of distinct developmental and evolutionary processes are separable, even within the shape of a single leaf. Here, we measured the shapes of > 3200 leaves representing > 270 vines from wild relatives of domesticated grape (Vitis spp.) to determine whether leaf shapes attributable to genetics and development are separable from each other. We isolated latent shapes (multivariate signatures that vary independently from each other) embedded within the overall shape of leaves. These latent shapes can predict developmental stages independent from species identity and vice versa. Shapes predictive of development were then used to stage leaves from 1200 varieties of domesticated grape (Vitis vinifera ), revealing that changes in timing underlie leaf shape diversity. Our results indicate that distinct latent shapes combine to produce a composite morphology in leaves, and that developmental and evolutionary contributions to shape vary independently from each other.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.021 |
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".