Ripening season affects tissue mineral concentration and nutrient partitioning in peach trees
Bibliographic record
Abstract
Abstract The objective of this research was to determine the influence of ripening season on nutrient concentration and nutrient partitioning of peach trees ( Prunus persica L. Batsch). We selected peach trees from three different ripening seasons and measured: (1) the concentration of macronutrients in pruned wood, thinned fruitlets, harvested fruit, and leaves fallen in autumn and (2) the total amount of macronutrients at each of these removal events. Our results showed that early‐season cultivars had more K in pruned wood, more P and K in fallen leaves, and more N, P, K, and Mg in mature fruits than mid‐ and late‐season cultivars. Also, early‐season cultivars removed more dry weight from pruned wood and fallen leaves but less from fruit than mid‐ and late‐season cultivars. These results suggest that different ripening season can affect peach tree nutrient concentration and nutrient partitioning and, consequently, peach cultivars harvested at different times of the year may benefit from specific fertilization programs rather than uniform, calendar‐based fertilization programs.
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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 | 0.000 | 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 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".