Pre- and post-harvest processing of medicinal plants
Bibliographic record
Abstract
Herbal medicine is used worldwide either as a sole treatment method or as part of a comprehensive treatment plan alongside orthodox methods of diagnosis and treatment. A survey reported that, in the USA, nearly one-sixth of women took at least one herbal product in 2000. Despite their widespread use, numerous reports show that the herbal products available to consumers are of variable quality. This disparity in quality of herbal preparations can be attributed to the fact that their production is complicated. To produce high-quality herbal products, attention must be paid to, among others, phytochemical variations due to plant breed, organ specificity, stages of growth, cultivation parameters, contamination by microbial and chemical agents, substitution, adulteration with synthetic drugs, heavy metal contamination, storage and extraction. This review focuses on organ specificity, seasonal variations, the effect of drying and storage, and the extraction of phytochemical constituents. Special emphasis is placed on the four most frequently used herbal products in the USA: echinacea, Ginkgo biloba, ginseng and St John's Wort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".