Leaf Area Estimation of Garden Boldo From Linear Dimensions
Bibliographic record
Abstract
The objective of this work was to determine a mathematical equation using linear measures that allows estimating a leaf area of the specie Plectranthus barbatus Andrews, a plant with medicinal properties popularly known as garden boldo. For this was performed a direct measurement of the leaf blade considering the length (L) along the midrib and the maximum width (W) perpendicular to the midrib of 500 leaves of different specimens and the observed foliar area (OLA), which were obtained by digitized images. A regression study with linear, quadratic, potential and exponential models was performed using a random sample of 400 from the evaluated leaves using OLA as a function of L, W or LW and then obtaining the estimated leaf area (ELA) of each model. From the remaining 100 leaves a validation of the tested models was performed using ELA as a function of OLA in a simple linear regression. From the residues between ELA and OLA the root-square-mean error and Willmot index (d) was obtained and the normality was verified. The parameters used for validation were: statistically linear and angular coefficient equal to zero and one respectively; coefficient of determination closest to the unit; RQME closer to zero; d index closest to the unit; normal distribution of residues. The equation that best represents the estimated leaf area of the garden boldo is ELA = 0.1389 + 0.6779 (LW).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".