Bibliographic record
Abstract
Ecologically important traits do not evolve without limits. Instead, evolution is constrained by the set of available and viable phenotypes. In particular, natural selection may only favour a narrow range of adaptive optima constrained within selective regimes. Here, I integrate data with theory to test whether selection explains phenotypic constraint. A global database of 599 plant species from 94 families shows that stomatal ratio, a trait affecting photosynthesis and defence against pathogens, is highly constrained. Most plants have their stomata on the lower leaf surface (hypostomy), but species with half their stomata on each surface (amphistomy) form a distinct mode in the trait distribution. A model based on a trade-off between maximizing photosynthesis and a fitness cost of upper stomata predicts a limited number of adaptive solutions, leading to a multimodal trait distribution. Phylogenetic comparisons show that amphistomy is the most common among fast-growing species, supporting the view that CO2 diffusion is under strong selection. These results indicate that selective optima stay within a relatively stable set of selective regimes over macroevolutionary time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".