Phytochemical screening of stem bark of valuable medicinal tree of tropical forest-Pterocarpus marsupium (Roxb.)
Bibliographic record
Abstract
Pterocarpus marsupium has known traditional and ethnobotanical uses since past thousands of years. The present study was carried out to screen the stem bark samples for different phytochemicals using different solvents. Bark samples were collected from different forest divisions of Madhya Pradesh and processed. The powdered samples were subjected to extraction with eight solvents of increasing polarity i.e. distilled water, ethanol, methanol, ethyl acetate, chloroform, benzene, hexane and petroleum ether. These extracts were evaluated for phytochemicals qualitatively as well as quantitively. Results indicated the extraction of phytochemicals better in polar solvents i.e. distilled water, ethanol and methanol. Moreover, the extracts of these solvents were found to contain saponins, tannins, alkaloids, flavonoids, terpenoids, steroids and phenols of pharmacological importance. Quantitative estimation of the totalphenol (%), flavonoids (%) and alkaloids (µg/100g) revealed high range of variation within as well as between the sampling sites. This indicates influence of genotype, environment and GxE interaction on the phytochemicals.
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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 | 0.000 |
| 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".