Determination of the Leaflet Area of Schinus terebinthifolius Raddi in Function of Linear Dimensions
Bibliographic record
Abstract
The objective of this study was to select mathematical equations that best fit the estimation of the leaf area of pink pepper (Schinus terebinthifolius Raddi) from the linear leaflet dimensions. 500 leaflets with different physiological ages of a commercial plantation were collected, located in the region of Gameleira, municipality of São Mateus, North of the State of Espírito Santo, Brazil. Was measured the length (L) along the main midrib, the largest width (W) and the observed leaf area (OLA) of each sheet. The product of the multiplication between L and W of the leaflets (LW) was determined. For the modeling the measurements of 400 leaflets were used, where OLA was used in function of L, W or LW. Based on the models found, we obtained the estimated leaf area (ELA). A simple linear regression was fitted for each proposed model of OLA in function of ELA. We tested the hypotheses H0: β0 = 0 versus Ha: β0 ≠ 0 and H0: β1 = 1 versus Ha: β1 ≠ 1, using Student’s t test at 5% probability. The mean values of ELA and OLA were compared by Student’s t test 5% probability. It was determined the mean error (E), mean absolute error (MAE), root mean square error (RMSE) and Willmott d index. The best adjusted equation was chosen by linear coefficient (β0) not different from zero, angular coefficient (β1) not unlike one, non-significant values of ELA and OLA, E, EAM and RQME closer to zero and Willmott’s index d closer to one. In this way, the leaf area of leaflets of Schinus terebinthifolius Raddi can be estimated by the quadratic model equation ELA = -2.6646 + 2.2124W + 1.3953(W)2 , using only the width of the leaves as a measure.
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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 | 0.000 |
| Open science | 0.000 | 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".