Development of Digital Central Innovation for Robotic VCDLN (DCIRV) in the Artificial Intelligence Era
Bibliographic record
Abstract
Continuous innovation has been built since 2020 with the development of VCDLN which is intended for developers and users of digital sources widely in Indonesia. This innovation was continued in 2024, with the support of AR and VR Technology based on Artificial Intelligence (AI) developed at the UPI Cibiru Campus. With the support of AR and VR experts, this innovation research product is called DCIRV (Digital Central Innovation for Robotics). This Innovation Research was carried out with a Mix-Method approach to meet the needs of prototype design and educational industry products as well as expert and user testing from the Nusantara region. To measure the quality of innovation products, it has been measured by experts from Bordeaux University France, Kitakyushu University, and McGill University. The findings of the prototype and the DCIRV research findings model, it show that starting from the needs analysis stage, development stage, validation stage, evaluation, and dissemination, DCIRV research products can be recommended as a solution for expanding access, services, and adding digital learning communities throughout the archipelago and even internationally.
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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 | 0.011 | 0.009 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".