Bibliographic record
Abstract
Labrador tea shoots has long been used in folk medicine as an antispasmodic, diuretic, diaphoretic, disinfectant, anti-inflammatory, soothing and antitussive remedy. In general it is due to the presence of terpene compounds, phenoliccompounds, tannins and other substances in the plant material. “Ledin” was the only one medicine that was produced from this raw material. It based on the essential oil and used as antitussive remedy. But for now, just only packed raw materials remain on drugstores shelves. That is why it is necessary to obtain standardized, dry extract from this raw material. We have already studied phenolic compounds essential oil, amino acids and polysaccharides in this plant. So the next step of our research was to investigate the composition of organic acids in dry extract of labrador tea shoots. By PC method the malonic, malic and citric acids were identified in dry extract. By chromatographymassspectrometry method the 26 organic acids were identified in dry extract. Total content of them was 296.16 g/kg. The dominant compounds were levulinic acid (9.62 %), malic acid (4.48 %), citric acid (8.46 %) and 4 oksy benzoicacid (2.03 %). Obtained results of the investigations of the qualitative composition and quantitative content of organic acids in dry extract from Ledum palustre shoots show prospects for further research and the possibility of developing new drugs and will be used for future standardization of the extract.
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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) | 0.001 | 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) | 0.002 | 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".