A Pharmacognostic, Phytochemical Study of Godhuma Bija (Triticum Aestivum Linn.)
Bibliographic record
Abstract
Background: Godhuma Bija (Triticum aestivum Linn.)are used for various conditions of ailments in traditional systems of medicine since ancient times. Pharmacognostic and phytochemical studies make the drugs more standardized and evidence based. Aims: This study was designed to lay down the various pharmacognostic and phytochemical standards which will be helpful to ensure the purity, safety, and efficacy of this medicinal plant. Materials and Methods: Various methods including macroscopic, microscopic, physicochemical and phytochemical methods were applied to determine the diagnostic features for the identification and standardization of intact and powdered drug of Godhuma Bija (Triticum aestivum Linn.) Results: The shape, colour, odour and surface characteristics were determined for the intact drug and powdered materials of Godhuma Bija (Triticum aestivum Linn.) Light and electron microscope images of cross-section of stamen and powdered microscopy revealed useful diagnostic features. Phytochemical, physicochemical analysis of powdered drug proved useful to differentiate the powdered drug material. High performance thin layer chromatography analysis showed the presence of important phytoconstituents. Conclusion: Morphology as well as various pharmacognostic aspects of Godhuma Bija (Triticum aestivum Linn.) of the plant were studied and have been described here along with phytochemical and physicochemical studies, which will help in authentication and quality control.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".