A New Mathematical Modeling of Banana Fruit and Comparison with Actual Values of Dimensional Properties
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Bibliographic record
Abstract
Banana (Cavendish variety) volume, projected area and surface area were estimated by mathematical approximation. The actual volume of banana was measured using water displacement, also the actual projected area and surface area were measured by image processing technique. These parameters that calculated by mathematical method compared to the actual values by the paired t-test and the Bland-Altman approach. The estimated volume and projected area were not significantly different from the volume determined using water displacement (P > 0.05) and projected area measured by image processing technique (P> 0.05) respectively. Although the estimated surface area was significantly different from the measured surface area by image processing method, but this mathematical estimation represented a good approximation of actual surface area. The mean difference between estimation method and water displacement method was 1.58 cm3(95% confidence interval:- 0.011 and 3.18 cm3 ; P = 0.058 ). There was a mean difference of - 0.71 cm2 (95% confidence interval: -1.49 and 0.074cm2 ; P = 0.083) between mathematical estimation method and image processing technique for projected area and 2.33 cm2 (95% confidence interval: 0.3 and 4.6 cm2 ; P < 0.05) for surface area. Water displacement is time-consuming method, also absorbed water by banana is affected on its properties. Image processing technique is very costly method but mathematical estimation does not require to expensive apparatuses.
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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 | 0.000 | 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 | 0.000 |
| 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 it