Analysis of the Production Chain of Bean Culture in Brazil
Bibliographic record
Abstract
This bibliographical review brings information about the productive chain of beans, an essential product in the Brazilian diet, but present in several countries of the world. Beans are a source of protein, fiber, minerals and vitamins, allowing for healthy eating for easy access. Being, Brazil is the third largest producer of beans in the world, losing only to Myanmar and India, also the main consumer of beans in the world, having to import part of the beans consumed in Argentina. Due to iss, the bean culture is a promising crop, since the supply does not meet the domestic demand, being necessary the application of technologies that improve the yield and facilitate the cultivation. One of the obstacles of the crop is the susceptibility of the deterioration of the grains to store them, because, these grains stored, the commercial value tends to fall due to loss of quality. The objective of the work was to raise information on the productive chain of the beans. Periodicals, books, and information literatures were explored. Therefore, in view of the above, it was observed that in Brazil, the crop still needs to stimulate cultivation to supply domestic demand, improve storage conditions, invest in genetic improvement to maintain grain quality, and the study is necessary of new alternatives of destination for the residues generated by the processing and beneficiation of grains.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".