Mathematical Models for Leaf Area Estimates of Guava
Bibliographic record
Abstract
The aim of this work was to compare methods of determining the leaf area of guava (leaf discs and scanned images) and to model leaf area as a function of linear dimensions. Four areas of guava ‘Paluma’ were selected (12, 15, 20 and 24 months of age) for the experiment in the municipality of Pedro Canário, ES, Brazil. We randomly collected samples from 15 plants in each area. Ten leaves were chosen among the lower, middle and upper thirds of each plant to ensure that leaves of all sizes were collected, for a total of 600 leaves. Subsequently, we determined the leaf area by the methods of digital imaging and leaf discs. Linear regression analysis and correlation analysis were used to compare the methods. Linear, quadratic and power models of leaf area, as a function of the length or width and/or the product of length and width were adjusted. The methods of leaf discs and scanned images are discordant. The method of digitised images was a better fit to the width of the leaf, while the method of leaf discs was a better fit for length.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".