Integrating Soil, Leaf, Fruitlet, and Fruit Nutrients, Along with Fruit Quality, to Predict Post-Storage Quality of Staccato Sweet Cherries
Why this work is in the frame
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Bibliographic record
Abstract
Predicting the post-storage quality of cherry fruits is crucial for determining their suitability for long-distance shipping or domestic distribution. This study aimed to forecast key quality attributes of Staccato sweet cherries after storage, simulating shipping conditions, by analyzing spring soil, leaf, fruitlet, and at-harvest data from thirty orchards in the Okanagan Valley, British Columbia, Canada, over two years. A support vector machine (SVM) was used to predict post-storage variables, with pre-harvest and at-harvest data selected by a genetic algorithm. The SVM accurately predicted soluble solids (R2 = 0.88), firmness (R2 = 0.83), and acidity (R2 = 0.79) after four weeks of storage, as well as visual disorders like slip skin and stem browning. Spring soil properties (Ca, Mg), leaf (N, Ca, Mg, Fe, Zn, B), and fruitlet data (N, Ca, Mg, B) were key predictors. Leaf Ca was vital for firmness and total soluble solids (TSS) prediction, while N in leaves and fruitlets influenced firmness, acidity, and disorders. Leaf Zn helped predict weight and acidity/TSS ratio, and Mg impacted fruit color. Pre-harvest leaf nutrition measured 3–4 weeks before harvest, proved most effective in predicting post-storage quality.
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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