ANALYSIS OF SPATIAL DISTRIBUTION OF MEDICINAL PLANTS USING GIS-TECHNOLOGIES ON THE EXAMPLE OF LISINSKY SCIENTIFIC-EXPERIMENTAL FORESTRY
Bibliographic record
Abstract
Harvesting of medicinal-plant raw materials is one of the most promising areas for Russian producers, the market of which is characterized as developing one. Research on spatial analysis and definition of biological stock of medicinal-plant raw materials is made on the example of Lisinsky scientific-experimental forestry. The following medicinal plants: lily of the valley (Convallária majalis L.), St. John's wort (Hypéricum perforátum L.), valerian (Valeriána officinális L.), wood sorrel (Óxalis acetosella L.), Labrador tea (Ledum palustre L.), stinging nettle (Urtíca dióica L.) are widespread on the territory of the forestry and have potential commercial value. Data on stocks and territorial location of the medicinal plants were obtained with the use of geoinformation technologies. Biological stocks of medicinal raw materials is defined by the regional table for average long-term yield based on the types of growing conditions, forest types and taxonomic characteristics of plants. If we consider the maximum yield of one specific type of forest, we can say that Labrador tea and stinging nettle has the greatest mass. In the result, it was determined that harvesting of medicinal plants in the territory of forestry is possible for all the considered types of medicinal plants, which will increase the volumes of harvesting and storage of valuable raw materials. Inventory information and location of medicinal plants will enable to optimize the choice of the routes for the priority procurement of raw materials. Using GIS technology the total biological stock of the types of medicinal plants in Lisinsky scientific-experimental forestry was determined. Spatial analysis allowed determining the areas with the highest yield of medicinal plants. Geographic information systems can be used as a tool for monitoring, inventory, protection and organization of the industrial harvesting of medicinal raw materials. The developed technology can be used to determine the yield of mushrooms and wild berries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".