Predicting models for mass and volume of the sweet cherry (<i>Prunus avium</i>L.) fruits based on some physical traits
Bibliographic record
Abstract
Khadivi-Khub, A. and Naderiboldaji, M. 2013. Predicting models for mass and volume of the sweet cherry ( Prunus avium L . ) fruits based on some physical traits. Can. J. Plant Sci. 93: 831–838. There are instances when it is desirable to determine relationships among fruit physical attributes. For example, fruits are often graded on the basis of size and projected area, but it may be more economical to develop a machine which grades by mass or volume. Therefore, the relationships between mass/volume (either mass or volume) and other physical attributes of fruit are needed. In this study three Iranian cultivars (Mashhad, Siah Mashhad, Siah Daneshkadeh), of sweet cherry were selected and the various models for predicting mass/volume of sweet cherry from its dimensions, projected areas, and volume/mass were established. The models were divided into three classifications: (1) single and multiple variable regressions of sweet cherry dimensions, (2) single and multiple variable regressions of projected areas and (3) estimating sweet cherry mass/volume based on its volume/mass. Moreover, some physical characteristics, such as dimensional characteristics, true density, bulk density, and porosity were determined with common methods. Results revealed that mass modeling based on minor diameter, three projected areas, and the measured volume are the best models. The highest determination coefficient in all the models was obtained for mass modeling based on measured volume as R2= 0.93. At last, mass modeling from an economic standpoint was recommended as the most reliable modeling.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".