A New Mathematical Modeling of Banana Fruit and Comparison with Actual Values of Dimensional Properties
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".