Methods for the Preparation of Recipes and its Uses for Curing Different Diseases Reported from District Bannu
Bibliographic record
Abstract
In the present study 47 recopies belonging to 44 genera, and 34 families were studied. Plant name, constituents, preparation method, frequency distribution and recipes multifunctional nature were given in methodical manner. The local population had good knowledge about the medicinal plant and appropriate time of collection. Younger generation is disregard about indigenous uses of different medicinal plants, but the old inhabitants still acquires knowledge about how to use the wild resources. The plants use for abdominal pain were Aloe vera, Chenopodium murale, Foeniculum vulgare, Lepidium sativaum, Portulaca oleraceae, Mentha longifolia, Menthe viridis, Papaver someniferum, Punica protopunica, Rumex hestatus, Thymus sarphylum and Verbescum thapsus. Some were used as tonic like Acacia modesta, Calotropis proceera, Olea ferruginea, Zathoxylum armatum, Melia azedarach, Tribulus terrestris, Vitex negundo, and Teucrium stocksianum, while other were used against diarhoea i.e Punica protopunica, Verbascum thapsus, Quercus incana, Plantago lanceolata, Pinus roxurghii, Papver somniferum, Myrtus cummunis and Mentha longifolia were commonly used plants. For body cooling Adiantum capallis veneris, Ajuga breteosa, Cichorium intybus, Portulaca oleraceae, Nasturtium officinale, Pistacia integerrima and Tribulus terrestris were frequently used. Similarly plants used as expectorant were Justacia adhatoda, Calotropis procera, Pinus roxburghii, and Zyziphus sativa, while plants used as antispasmodic were Justacia adhatoda, Berberis lycium and Datura stramonium, the plants used as jaundice were Berberis lycium, Cichorium intybus, Nasturtium officinale, Pistacia integerrima, and Teurium stocksianum, while Verbascum thapsus, Zizphus sativa, and Salvia moorcrotiana were used as emollient. The vegetation of the area was found to be under high biotic pressure such as deforestation and overgrazing. Ruthless collection of medicinal plants had threatened their existence and more plants are becoming vulnerable due to the destruction of their habitat.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".