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Record W2137180576 · doi:10.1079/pgr200569

Pre- and post-harvest processing of medicinal plants

2005· article· en· W2137180576 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePlant Genetic Resources · 2005
Typearticle
Languageen
FieldMedicine
TopicGinkgo biloba and Cashew Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPhytochemicalTraditional medicineBiologyGinsengBiotechnologyMedicinal plantsGinkgo bilobaToxicologyMedicineBotanyAlternative medicine

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.249
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it