Diversity of NTFPs and Their Utilization in Adilabad District of Andhra Pradesh, India
Bibliographic record
Abstract
Adilabad in Andhra Pradesh is a backward district, with 37.72% of geographic area under forest cover and inhabited by 17.08% ethnic people who use the local tropical dry deciduous forests to extract Non-Timber Forest Products (NTFPs) for self-consumption and economic subsistence. The analysis of NTFPs in six forest divisions of Adilabad district, viz. Adilabad, Bellampalli, Jannaram, Kagaznagar, Mancherial and Nirmal reveals the use of consumptive category of goods like wild food plants, honey, oils, fodder, etc. on one hand and the non-consumptive items like gums, resins, gum-resins, dyes, wax, lac, fibers, fuel wood, charcoal, fencing material, brooms, wildlife products, raw materials like bamboo and cane for handicrafts, etc. besides the medicinal plants. The NTFP diversity shows the cognitive ability of the people while the products extracted belong to 183 flowering plant species which represent 149 genera of 64 families (164 Magnoliopsida; 19 Liliopsida). The Legumes dominate the list with 31 taxa, followed by Rubiaceae (11) and Euphorbiaceae (7). Most of the NTFP species are phanerophytes (61% trees) and indigenous. The government of Andhra Pradesh has a procurement policy and price index for select NTFPs by which the stakeholders get reasonable seasonal income through the collection and sale of beedi leaf, gums (karaya, thiruman, konda gogu), stem bark (narra mamidi), fleshy corolla (ippa), fruits (karakkaya, kunkudu), seeds (chilla, mushti, morli), etc.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".