Vein-to-blade ratio is an allometric indicator of climate-induced changes in grapevine leaf size and shape
Bibliographic record
Abstract
ABSTRACT Premise As a leaf expands, its shape dynamically changes. Previously, we documented an allometric relationship between vein and blade area in grapevine leaves. Larger leaves have a smaller ratio of primary and secondary vein area relative to blade area compared to smaller leaves. We sought to use allometry as an indicator of leaf size to measure the environmental effects of climate on grapevine leaf morphology. Methods We measure the ratio of vein-to-blade area in 8,412 leaves from the same 208 vines across four growing seasons (2013, 2015, 2016, and 2017) using 21 homologous landmarks. Matching leaves by vine and node, we correlate size and shape of grapevine leaves with climate variables. Key results Vein-to-blade ratio varies strongly between years in ways that blade or vein area do not. Maximum daily temperature and to a lesser degree precipitation are the most strongly correlated climate variables with vein-to-blade ratio, indicating that smaller leaves are associated with heat waves and drought. Leaf count and overall leaf area of shoots and the vineyard population studied also diminish with heat and drought. Grapevine leaf primordia initiate in buds the year prior to when they emerge, and we find that climate during the previous growing season exerts the largest statistical effects over these relationships. Conclusions Our results demonstrate the profound effects of heat and drought on the vegetative morphology of grapevines and show that vein-to-blade ratio is a strong allometric indicator of the effects of climate on grapevine leaf morphology.
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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 | 0.001 |
| 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".