Geographical Indications in Horticulture: North East India Perspective
Bibliographic record
Abstract
A geographical indication (GI) is a sign used on products that have a specific geographical origin and possess qualities or a reputation that are due to that origin (WIPO).Geographical Indication, an exclusive community rights, recognizes the importance of location, climate and human know how in making the products distinguished on the basis of their unique intrinsic attributes. It acts as an effective tool in protecting and rewarding the market potential of elite items and also the traditional knowledge associated with them (Kishore,2018).Since the enactment of the Geographical Indications of Goods(Registration and Protection) Act, 108 agricultural items have been accorded with GI tags till date and among them the horticultural items has its share of more than 75 percent. Among horticultural crops, maximum GIs have been accorded to fruit crops (38nos) followed by Plantation crops(14nos).Vegetable crops and spices share 12 and 13 GI tags whereas flower crops and aromatic plants conferred with 5 and 3 GI tags, respectively. North East India comprising of eight states is rich in diversity of many fruits, vegetables, flowers particularly orchids, spices and medicinal plants (Deka et al., 2012).It is evident from the list of registered GIs, how other states have quickly grabbed the opportunity and secured GI tagging of their goods and produce peculiar to their region. However, Northeast India, inspite of its rich horticultural diversity have not been able to take advantage of GI tagging. The horticultural produce of Northeast India got leg up with as many as 12 products accorded GI tags. GI tags will pave the way for better branding and marketing of these horticultural products both in domestic and international Market, besides protecting local crops and facilitating better return to legitimate rural producers. Thus, it is pertinent that a streamlined strategy should be adopted for tapping the untapped potential of the registered GI products, because unless that is done, the previous, ongoing as well as future registrations will have no sustainability (Sharma and Rajan, 2018).
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".