Investigating the environment conditions of medicinal plants based on biodiversity and indigenous knowledge
Bibliographic record
Abstract
The protection of medicinal plants has been effective as a key factor in preserving the environment of medicinal plants. As such, this paper aims to map the environment of medicinal plants based on biodiversity and indigenous knowledge, in which its role is constantly seen in environmental studies. The study method was based on field survey in the target areas: Morvarid, Heiderabad, Dehmoord and Fath al-Mubin in Darab city in Fars province. Based on the findings of the study, a total of 89 species belonging to 43 families in the target areas were identified, with the highest frequency belonging to the mint family. According to the results of studies, anghozeh, baneh, thyme Shirazi, arjan, kenar, Jashir (Prangos), lemon balm, Myrtus, cumin and kakuti need protective measures. Combining indigenous plant ethnological knowledge with new technologies along with high genetic diversity will be the way to control damage and protect the effective genes of medicinal plants. Ultimately, the elimination of the inheritance of desirable plant genes will lead to the erosive growth and acceleration of the regression of plant cover, which is considered as a rich chain and preserver of soil sanctity and stability of nature in the environment.
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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) | 0.000 | 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 | 0.001 |
| 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".