<i>LeafArea</i> : an R package for rapid digital image analysis of leaf area
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Measuring leaf area is essential to quantifying other leaf functional traits. This paper introduces a new R package, LeafArea , which allows one to conveniently run ImageJ ( http://imagej.nih.gov/ij/ ) within R. The functions in this package analyze multiple scanned leaf images in the target directory, generate multiple output files containing the leaf area of each leaf image, and then process and combine these files into a single file in a format that is convenient for subsequent analyses. Leaf area data from multiple images from the same sample can be combined automatically. This function allows users to cut large leaves into several pieces during scanning. The package provides a user‐friendly, automated tool for measuring leaf area from scanned images.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it