Development of Digital Central Innovation for Robotic VCDLN (DCIRV) in the Artificial Intelligence Era
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it