Leaf anatomy is associated with the type of growth form in Neotropical savanna plants
Bibliographic record
Abstract
Scleromorphic leaf structures are associated with plants growing under drought-prone conditions, high irradiances, and nutrient-poor soils. Sclerophylly can also be a valuable deterrent against herbivores. However, comprehensive studies of leaf anatomical traits encompassing different growth forms are lacking. The savannas of central Brazil (Cerrado) are characterized by high species diversity and a variety of growth forms. We performed a field study to characterize leaf anatomical traits and leaf mass per area (LMA) in 57 co-occurring Cerrado species of distinct plant families (located at different positions in the angiosperm phylogeny) and categorized according to the following growth forms: trees, palms, shrubs, subshrubs, vines, grasses and herbs. Plant growth form and leaf structure were interrelated. Four groups with distinct leaf anatomical syndromes were identified by NMDS analysis: grasses, palms, herbaceous (herbs, vines, and most subshrubs), and woody (trees and shrubs) plants. Trees and shrubs had scleromorphic dorsiventral leaves, with high tissue thickness and LMA. Herbaceous plants had thin, mesomorphic leaves. While most herbaceous plants had dorsiventral type of mesophyll, grasses and palms had homogenous mesophyll with sclerenchymatous bundle sheath extensions. Palms differed from grasses by having thicker leaves and sclerenchymatous hypodermis. In conclusion, Neotropical savannas cannot be exclusively described as scleromorphic vegetation.
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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".