Computational Method for Quantifying Growth Patterns at the Adaxial Leaf Surface in Three Dimensions
Bibliographic record
Abstract
Growth patterns vary in space and time as an organ develops, leading to shape and size changes. Quantifying spatiotemporal variations in organ growth throughout development is therefore crucial to understand how organ shape is controlled. We present a novel method and computational tools to quantify spatial patterns of growth from three-dimensional data at the adaxial surface of leaves. Growth patterns are first calculated by semiautomatically tracking microscopic fluorescent particles applied to the leaf surface. Results from multiple leaf samples are then combined to generate mean maps of various growth descriptors, including relative growth, directionality, and anisotropy. The method was applied to the first rosette leaf of Arabidopsis (Arabidopsis thaliana) and revealed clear spatiotemporal patterns, which can be interpreted in terms of gradients in concentrations of growth-regulating substances. As surface growth is tracked in three dimensions, the method is applicable to young leaves as they first emerge and to nonflat leaves. The semiautomated software tools developed allow for a high throughput of data, and the algorithms for generating mean maps of growth open the way for standardized comparative analyses of growth patterns.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".