Medicinal Plants: Trade and Commerce Opportunities with India
Bibliographic record
Abstract
About 5,000 plant species have been documented for medicinal value and phyto-chemically studied. Of these, 1,100 are used in different systems of medicine, 600-700 are used in indigenous industries, but only about 150 have been commercially exploited. Besides domestic use, export potential of these plants is huge and given a quality upgadation of such drugs, competitiveness and globalization is ensured. There is, however need for doing scientific work on their pharmacology, phytochemisty and clinical experiments to develop the export potential fully. Important plant species being utilized at present, their world prices, and other potential species have been listed. Shift towards use of herbal drugs worldwide has been noted. There is good scope in developing this sector. Trade and commerce requirements relating to export and marketing in various foreign marketing ego Canada, Hungary, France, UK, USA have been discussed with a view to developing trade in these countries in medicinal products. Various measures, which handicap expansion, have been pointed out. These are: agricultural practices like harvesting and propagation, processing high yield varieties, quality control, marketing, training of personnel, equipment and knowledge about latest advances in technology etc. where efforts need to be focussed.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".