Effect of nanosized calcium and magnesium particles on absorption in peach tree leaves
Bibliographic record
Abstract
Plants require a variety of elements to grow. Of these, calcium (Ca) and magnesium (Mg) play an important role in strengthening the cell wall. Although peaches (Prunus Persica) are highly preferred by consumers, they ripen quickly and become soft over a relatively short period of time after harvesting, making them difficult to transport and store. In addition, the ripening process of peaches proceeds very quickly; cell walls are weakened during maturation, and various pathogens can easily grow, causing rapid decay. Therefore, yield loss occurs during long-term storage or transport. To increase the storage period, a method to delay softening action is required. One potential means to improve firmness is to improve Ca and Mg content as these elements make up and strengthen cell walls. However, Ca and Mgare not readily absorbed by plants. In this study, the size of Ca and Mg particles were reduced to less than 900 nm via grinding and their absorption rates were evaluated in the leaves of peach trees. When plant nutrients with a small particle size by nanotechnology were sprayed on peach trees, the content of Ca and Mg was increased in the petioles, adaxial, abaxial, and leaf side. Therefore, a reduction in the particle size of Ca and Mg increases the absorption rate in peach leaves.
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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 | 0.000 | 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) | 0.001 | 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".