Importance of Non-Timber Forest Products in Tribal Livelihood of Mayurbhanj District of Odisha
Bibliographic record
Abstract
Non-Timber Forest Products (NTFPs) are crucial for the livelihoods of tribal communities in the Mayurbhanj District of Odisha. This district, rich in forest resources, is home to a significant tribal population whose economic, cultural, and social well-being is intricately tied to the collection and utilization of NTFPs. These products, which include fruits, medicinal plants, bamboo, honey, and fibers, serve as vital sources of income, food security, and traditional medicine. NTFPs also promote social cohesion within communities, with women playing a central role in their collection and trade. NTFPs play an important role in the livelihoods of many communities around the world, providing food, income, medicine and other resources. They are also important for biodiversity conservation and sustainable forest management. This study aimed to document the traditional uses, phytochemical structures, collection methods, processing techniques, and marketing strategies of 92 species belonging to 46 families of Non-Timber Forest Products (NTFPs) found in a Rasgovindpur Block, Mayurbhanj, Odisha. The data was collected through interviews with local communities and experts, as well as literature reviews. It is primarily based on field surveys carried out Rasgovindpur Block, mainly in three panchayat Badampur, Raghabpur and Totapada, where dwellers provided information on plant species used in household materials. The traditional uses of these NTFPs were categorized based on their medicinal, culinary, cosmetic, and other applications. First-hand information on medicinal uses was gathered from knowledgeable tribals, rural and traditional healers (Kabiraj) through semi structured questionnaire. Plants contain numerous biologically active compounds which are help improve human life so, major phytochemical compound structures were given here. For the description of specific QR code generate for each of the plant species., while collection methods, processing techniques, and marketing strategies were described based on local practices and market trends. It is re-stressed that pharmacological and phytochemical investigations may be undertaken on all these reported plants species to validate the claims. The information provided may also help in the discovery of new drugs of plant origin.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".