Protective Effect of the Castanha-de-cutia (Couepia edulis Prance) in Regards to Metabolic Illnesses and Its Socioeconomic Aspects: A Systematic Review
Bibliographic record
Abstract
The biodiversity of the Amazon region includes many oleaginous fruits, among them the Castanha-de-cutia, whose great potential is its almonds, which have about 16.6% protein and 73% oil. What is also remarkable is the exceptional amount of bioactive compounds in their chemical composition. This bibliographic review has the objective of arousing interest in the consumption of the Castanha-de-cutia almonds in order to improve quality of life. A systematic search was carried out for studies published between 1947 and 2016 that examined the physical-chemical characteristics, nutritional composition, bioactive functions and industrial potential of the almond from the Castanha-de-cutia. These studies included the use of this Amazonian fruit in the diet of traditional populations in the Amazon region. The presence of essential fatty acids, tocopherols, carotenoids and phenolic compounds strengthens the endogenous system, reducing oxidative stress and the risk of pathologies. The high content of monounsaturated fatty acids was also verified. This demonstrated exceptional nutritional value for the Castanha-de-cutia oil of Amazonian origin. The identification of the bioactive compounds of the Castanha-de-cutia almond is of fundamental importance as an alternative source of such compounds, since these desirable characteristics can be used in the prevention and treatment of metabolic diseases, as well as having great socioeconomic potential in the paint, food and pharmaceutical industries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".