Morphological Characteristics of Varieties of Sweet Potato, Cenoura, Margarita, Rainha and Roxa, Cultivated in Amazonas, Brazil
Bibliographic record
Abstract
Sweet potato [Ipomoea batatas (L.) Lam.] is a nutritious food, from an easy-to-handle cultivation, present in all Brazilian regions, with great socioeconomic relevance. There is a wide genetic diversity of sweet potato, expressing different flesh and skin colors, and containing different nutritional values. However, only sweet potatoes with cream flesh and white or purple skin are present in the Manaus market. It is important to promote the visibility of the varieties that are being cultivated in a less expressive way, often only by the indigenous people. In this way, the valorization of the product and the conservation of agrobiodiversity will be encouraged. One obstacle to identifying varieties is that the same variety can have different names, and the same name can be given to different varieties. Therefore, it is essential that a morphological description of the varieties present locally is made. This will be a subsidy for future research and actions aimed at the development of the primary sector that has the sweet potato as its object. Given the above, this work aimed to morphologically describe the sweet potato varieties Cenoura, Margarita, Rainha and Roxa present at the Experimental Farm of the Federal University of Amazonas. The Cenoura, Rainha and Roxa varieties have different skin and/or flesh colors and a tuberous root shape, catering to different consumer tastes. The Cenoura variety (skin and orange flesh) has the potential to be cultivated by Amazonian farmers and introduced in the local market (Alves, 2021; Filgueira, 2008; Huamán, 1991).
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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 | 0.001 |
| 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".