Effects of Nitrogen on the Yield and Quality of Selected Chinese Medicinal Plants of the Lamiaceae Family
Bibliographic record
Abstract
With many of the practicing acupuncturists in the United States prescribing herbal formulas, the demand for Chinese medicinal plants has been increasing. In the past several years, however, quality concerns have been raised about medicinal plants imported from China. To assure the safe and efficacious care for patients, practitioners need good quality plant material produced under controlled and documented conditions in accordance with good agricultural practices. The objective of this research was to determine whether quality plant material of selected species of Chinese medicinal plants could be cultivated in the northeastern United States and whether such cultivation was economically feasible. For these reasons, Agastache rugosa (Fisch. & C.A. Mey.) Kuntze, Leonurus heterophyllus Sweet, L. sibiricus L., and Schizonepeta tenuifolia Briq. were field grown in a randomized complete block design using 0, 100, and 200 kg ha −1 of nitrogen supplied as soybean meal. The nitrogen treatments resulted in dose-related increases in yield in all species. Preliminary organoleptic evaluation (color, aroma, taste, cleanliness) suggests the cultivated Chinese medicinal plants were of higher quality than commercially available plant material imported from China.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".