Determination and Evaluation of Se-Rich High-Quality Rice Produced by Compound Nutrient Solution
Bibliographic record
Abstract
Rice is one of the most important food crops in the world. Its biggest flaw is the low content of protein and essential amino acids, which severely limits its nutritional value. In order to produce high-quality rice with rich Se, we sprayed different concentrations of compound nutrient solution (containing Se (selenium), amino acid compound, zinc and boron) on the rice at different growth stages; and then determined the main nutrient content of their polished rice. The results showed that spraying low concentration compound nutrient solution (Each liter contained 20 mg of Se, 333 mg of complex amino acids, 33 mg of zinc and 33 mg of boron) to rice in the heading stage produced rice with the highest total starch and fat content and lower amylose content. Spraying high concentration compound nutrient solution to rice during the filling stage produced rice with the highest Se content. Multiple sprays of compound nutrient solution produced rice with low protein and low starch. Spraying low concentration compound nutrient solution on rice in milky stage significantly increased the content of protein, total starch, fat, all essential amino acids (Lysine increased by more than 57%), amylopectin and Se in rice; significantly reduced amylose content; significantly improved the nutritional value and taste quality of rice. The conclusion is that spraying low concentration compound nutrient solution on rice in the milky stage can produce rice with the highest content of protein and essential amino acids, higher content of total starch, fat and Se, and the lowest amylose content; significantly improve the nutritional value and taste quality of rice. The technical solution can comprehensively and effectively improve the nutritional value and flavor quality of rice, and has great development and application value.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".