Determination of quality parameters in Hypericum perforatum grown in Saskatchewan
Bibliographic record
Abstract
Hypericum perforatum, a medicinal plant known as Saint John’s Wort, has been used extensively for its antidepressant activity in North America over the past three years. The objective of this study was to establish the influence of plant part and time of harvest on phytomedicinal quality of Saint John’s Wort grown in Saskatoon. Varietal influence on quality was also investigated. Flowering tops, upper leaves and stems, and lower leaves and stems of two and three years old plants, variety Standard, harvested at seven \ndifferent times from budding to post-blooming from June to September, 1998, were used for quality assessment. Extraction protocol for optimal recovery and an high performance liquid chromatography (HPLC) method for quantification of 7 marker compounds for Saint John’s Wort, hypericin and pseudohypericin, and 5 selected flavonoids (quercitrin, quercetin, rutin, hyperoside and biapigenin) were developed. The flowering tops followed by the upper most leaves contained the highest concentration of hypericins and flavonoids when harvested in late June, 0.35 % and 4.0%, respectively. The hypericins content declined by more than 90% between late June and end of August. The content of flavonoids showed a similar declining trend from early July onward. A correlation between date of harvest and quality, and plant part and quality was apparent. Two varieties, Anthos and Elixir™, were found superior in both plant yield and plant quality.
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 | 0.001 | 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".