Mineral and Metal Levels in Brown Sugar from Organic and Conventional Production Systems
Bibliographic record
Abstract
Food and nutrition are basic requirements for the promotion and protection of health. In addition to ensuring the availability of calories for consumption, it is necessary to improve the access to the variety of nutrients offered to the population. The consumption of brown sugar inserts nutrients into the feeding, once it is produced only by the evaporation of the existing water in the sugarcane broth, thus maintaining all the original constituents of the plant. To evaluate the influence of organic and conventional fertilization in the nutritional quality of the brown sugars, a test was conducted with six fertilization systems, in a completely randomized block design with four repetitions. The sugars produced were analysed regarding the contents of the nutrients Fe, Zn, Mn and Cu and the toxic elements Pb and Cd. The results showed that the different fertilization systems influenced the content of the minerals present. Although small, the differences were statistically significant and the treatments provided sugars with nutritional characteristics, with an advantage for the totally organic sugar (using organic fertilizer and corrective) which, in addition to environmental issues, did not present lead contamination.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".