Mean-variance relationships of leaf bilateral asymmetry for 35 species of plants and their implications
Bibliographic record
Abstract
Leaf bilateral asymmetry, an important indicator of leaves that tend to be affected by the above-ground architectural structure of plants and their environments (especially light), has been poorly studied. Taylor’s power law (TPL) describes a power-law relationship between the mean and variance of a non-negative random variable, and its exponent has been demonstrated to reflect the degree of heterogeneity of the branch spatial arrangement of plants. In this study, we checked whether the mean-variance relationship of the absolute difference in area between the left and right sides of 11396 leaves from 35 species of plants within four families followed TPL. TPL was found to hold true for each species investigated here, and the estimated TPL exponents fell within a range of 1.5 to 2.0. At the family level, there were no any significant differences in the estimated exponents of TPL among the pooled data of Lauraceae, Oleaceae, and Bambusoideae, but those exponent values were significantly smaller than that of Magnoliaceae. We also pooled the data from the four families, and we found that there was a general rule for the mean-variance relationship for the bilateral asymmetry among the studied broad-leaved plants. Given the variety of leaf bilateral asymmetry among species, our results highlight the importance of plant aboveground architecture and the heterogeneity of leaves on different positions to a better understanding of leaf development mechanism and how it responds to the surrounding environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".