Conference report: Dhara - Vision Ayurveda 2047
Bibliographic record
Abstract
The Dhara-Ayurveda 2047 conference was organized at the University of Trans-Disciplinary Health Sciences and Technology (TDU), Bengaluru on 23rd and September 24, 2022. This was a pioneering initiative of Ministry of Culture and Ministry of Ayush to raise public awareness about the contemporary relevance of India's medical heritage. The theme of the conference was to offer innovative approaches to strengthen and globalize Ayurveda by the year 2047 to commemorate 100th year of independence of India. More than 2000 delegates from academia and industries attended this event. This conference featured a range of components including insightful vision talks, educational exhibition, interactive practical sessions, innovation-focused competition, cultural programs, and health assessment program. Distinguished speakers shared their forward-looking perspectives on the future of Ayurveda in the year 2047 with respect to personalized nutrition, Ayurvedic healthcare, interdisciplinary medicine, AYUSH integration, Ayurvedic industry, and other related topics. The conference provided a platform for students to learn innovative approaches in Ayurveda and also awarded deserving winners for their ideas. Thus, Dhara-Ayurveda 2047 conference served as a valuable platform for sharing knowledge and exploring the future of Ayurveda in India across different disciplines related to Ayurveda like biomedical sciences and engineering, information technology, pharmaceutical sciences and folk healers to visualize Ayurveda in the year 2047.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.112 | 0.032 |
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".