HIGH TECHNOLOGIES AND ARTIFICIAL INTELLIGENCE IN THE MANUFACTURING SECTOR AND AGRICULTURE IN INDIA
Bibliographic record
Abstract
The article argues that the process of digitalization is gaining unprecedented prominence, having an unprecedented range of impacts: from everyday life to scientific activities.The author emphasizes that India is representative in this regard, as it is characterized by intensive technological growth, which results in a significant potential for the actualization of new technologies (in particular, in manufacturing and agriculture).It is noted that the integration of the latest technologies and artificial intelligence into the work of the latter is promising.The researcher emphasizes that today India is actively introducing the identified technologies (artificial intelligence, machine learning, Internet of Things, etc.) to perform certain functions to increase production (in particular, dairy).It is emphasized that this produces an exponential increase in the efficiency of conventional production processes, combined with the formation of new effective ways to improve product quality and avoid economic losses.The author argues that the introduction of high technologies and AI produces several parameterization features of its functioning and is a prospect for further research of the analyzed issues, in particular: a) personnel problems, which consist of proper training, continuous professional development, retraining, etc. of the workforce, and so on; b) equality of access to technology for all regions and sectors of the country's economy, without which India's sustainable development and reduction of the existing gap in the genesis of its enterprises (primarily small and medium-sized enterprises) are impossible; c) state support for the introduction of technologies, which is represented by the development of several programs to stimulate their actualization, innovative development, etc.; d) the production of social protection, Věda a perspektivy № 5( 36) 2024
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 | 0.000 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".