Determination and Analysis of the Content and Distribution of Mineral Elements in Black Rice by SEM-EDS
Bibliographic record
Abstract
Black rice has very superior medicinal value. Since ancient times, it has been used as a nourishing and health-care rice for medicine and food. It has powerful functions such as disease prevention, regulation of circadian rhythm, and promotion of physical recovery. It is suitable for long-term consumption. In this paper, optical microscope, scanning electron microscope and energy dispersive spectrometer (SEM-EDS) were used successively to visualize and quantitatively analyze the element distribution in the chalky and non-chalky areas of two indica rice varieties in Southern Henan. The results showed that black rice has rich C and O content, followed by N, P, S content, Mg, K, Ca, Mn, Zn content is less. The content of the O element in the chalky area is higher than that of the non-chalky area, while many elements such as C, N, P, S are significantly higher in the non-chalk areas than in the chalk areas; especially the N and S elements are the best indicators of protein, the content in chalkiness area was lower than that in non-chalky area. It can be inferred that the protein content in non-chalky part was higher than that in chalky part, that is, chalkiness character of black rice would affect the nutritional quality of rice. Therefore, our results showed the distribution of elements and protein in black rice, which is helpful for the cultivation of new high-quality black rice varieties in the future.
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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 | 0.000 |
| 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".