Future Prospect and Global Market Demand for Dried Herbs, Spices and Medicinal Plants
Bibliographic record
Abstract
Herbs, spices and medicinal plants are regarded as the real treasure for any country in the global market, owing to the quantum of exchange and the revenue associated with it. Herbs are non-woody stem herbaceous plants majorly used in food for culinary purposes. Spices are a group of vegetable products, rich in essential oils and aromatic principles, and are mainly used as condiments available as a whole or in a crushed or powdered form. Thus, it is needless to say that the demand for these medicinal plants, herbs and spices is immense and it is expected to grow in near future. Cumulatively, for the three commodities, China, India, Canada, United States and Germany are responsible for 60% of export quantity globally, and the United States, Germany, China, Japan and Singapore credit for 50% of worldwide import. This chapter deals with the discussion on the present scenario, the expected future demand and supply of these medicinal plants, herbs and spices.
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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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.011 |
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".