Estimation of Leaf Area of Jackfruit Through Non-destructive Method
Bibliographic record
Abstract
The objective of this study was to determine mathematical equations that estimate the leaf area of jackfruit (Artocarpus heterophyllus) in an easy and non-destructive way based on linear dimensions. In this way, 300 leaves of different sizes and in good sanitary condition of adult plants were collected at the Federal Institute of Espírito Santo, Campus Itapina, located in Colatina, municipality north of the State of Espírito Santo, Brazil. Were measured The length (L) along the midrib and the maximum leaf width (W), observed leaf area (OLA), besides the product of the multiplication of length with width (LW), length with length (LL) and width with width (WW). The models of linear equations of first degree, quadratic and power and their respective R2 were adjusted using OLA as dependent variable in function of L, W and LW, LL and WW as independent variable. The data were validated and the estimated leaf area (ELA) was obtained. The means of ELA and OLA were compared by Student’s t test (5% probability) and were evaluated by the mean absolute error (MAE) and root mean square error (RMSE) criteria. The choice of the best model was based on non-significant comparative values of ELA and OLA, in addition to the closest values of zero of EAM and RQME. The jackfruit leaf area estimate can be determined quickly, accurately and non-destructively by the linear first-order model with LW as the independent variable by equation ELA = 1.07451 + 0.71181(LW).
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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 | 0.001 |
| 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".