A Review on Phaseolus vulgaris Linn
Bibliographic record
Abstract
We usually take food daily to get energy for our daily activities, our food may be vegetables, fruits, meat, etc.Some vegetables and fruit besides providing flavour, smell, taste, to food, they serve as medicinal plant.Medicinal plant is any plant which in one or more of its organs contains substances that helps to synthesis of new drugs.1 Phaseolus vulgaris is one of the most consuming food crop and medicinal plant all around the world as it is popular because of its seed.Its extract has been used for treatment such as for weight reduction.2 In Folk medicine, Beans are said to be used for acne, bladder, burns, cardiac, carminative, depurative, diabetes, diarrhoea, diuretic, dropsy, dysentery, eczema, emollient, hiccups, itch, kidney stone re solvant, rheumatism, sciatica, and tenesmus.3 Phaseolus vulgaris Linn., has an high potential to be used as human and animal food and to be utilized as a pharmacological agent in medicine.In this paper, phytochemistry and pharmacological activities of this plant are reviewed and its potential for further investigation, exploitation, and utilization are discussed.Phaseolus vulgaris Linn., are grown in regions as diverse as Latin America, Africa, the Middle East, China, Europe, the United States, and Canada.The leading bean producer and consumer is Latin America, where beans are a traditional, significant food, especially in Brazil, Mexico, the Andean zone, Central America, and the Caribbean (Table 1).4,5
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".