Modeling Fruit Growth of ‘Triunfo’ Pear Grown in North Espírito Santo State
Bibliographic record
Abstract
The objective of this study was to obtain mathematical models to estimate non-destructively the fruit mass of pear cv. ‘Triunfo’. To this end, 128 fruits from all developmental stages collected at three different times were used. Fruits were measured for maximum length (L), maximum width (W) and observed mass (OM). For the adjustment, with a sample of 100 fruits, the models first degree linear, quadratic and power were tested, in which the OM was used as the dependent variable in function of L and W. From a sample of 28 fruits, separated for this purpose the equations were validated. Thus, it indicates an equation of the quadratic model represented by EM = 36.020218 – 3.067232(W) + 0.082568(W)2  using from the measurement of the largest fruit width (W), as the most accurate to estimate the fruit mass of pear cv. ‘Triunfo’.
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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 | 0.002 |
| 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".