Mathematical Modeling for Leaf Area Estimation From Papaya Seedlings ‘Golden THB’
Bibliographic record
Abstract
The aim of this study was to select the most suitable model for leaf area estimation from papaya seedlings cv. ‘Golden THB’ using linear dimensions of leaves with unilobular and trilobular morphology. It was used leaves of 60 seedlings with 30 days after sowing produced in nursery of the Fazenda Santa Teresinha which belongs to company Caliman Agrícola S.A., in the municipality of Linhares, state of Espírito Santo, in March 2016. The measurement of the length (L) was performed along the midrib, the maximum width (W) of the leaf blade, the product of the length by the width (LW) and the observed leaf area (OLA). From these results, first degree and power linear regression models was adjusted. From the proposed regression models, the validation was performed with a leaves sample of 60 seedlings produced in June 2016, obtaining, thus, the estimated leaf area (ELA). The following criteria were used to choose the best model: the highest coefficient of determination (R2), the values do not significant of the comparison of means of OLA and ELA and values of MAE and RMSE closer to zero. The leaf area estimation from papaya seedlings cv. ‘Golden THB’ can be represented through equation ELA = -0.402619 + 0.612525(LW) for trilobular leaves and through equation ELA = 0.623355 + 0.610552(LW) for unilobular leaves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".