Leaf architecture and functional traits for 122 species at the University of California Botanical Garden at Berkeley
Bibliographic record
Abstract
The dataset contains leaf venation architecture and functional traits for a phylogenetically diverse set of 122 plant species (including ferns, basal angiosperms, monocots, basal eudicots, asterids, and rosids) collected from the living collections of the University of California Botanical Garden at Berkeley (37.87° N, 122.23° W; CA, USA) from February to September 2021. The sampled species originated from all continents, except Antarctica, and are distributed in different growth forms (aquatic, herb, climbing, tree, shrub). The functional dataset comprises 31 traits (mechanical, hydraulic, anatomical, physiological, economical, and chemical) and describes six main leaf functional axes (hydraulic conductance, resistance and resilience to damages caused by drought and herbivory, mechanical support, and construction cost). It also describes how architecture features vary across venation networks. Our trait dataset is suitable for (1) functional and architectural characterization of plant species; (2) identification of venation architecture-function trade-offs; (3) investigation of evolutionary trends in leaf venation networks; and (4) mechanistic modeling of leaf function. Data are made available under the Open Data Commons Attribution License.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".