IM-MetaLAB: The First Digital Laboratory for Teaching the Fundamental Concepts of Instrumentation and Measurement in Metaverse
Bibliographic record
Abstract
This article presents IM-MetaLAB, an innovative virtual laboratory designed to teach fundamental concepts in instrumentation and measurement within an immersive metaverse environment. Overcoming the constraints of conventional online and remote labs, IM-MetaLAB offers a fully interactive 3D space where students engage with digital replicas of real laboratory instruments, enabling a realistic and engaging educational experience. Utilizing IoT technologies-specifically the Message Queuing Telemetry Transport (MQTT) protocol-the platform enables real-time synchronization between physical and virtual devices. Students can operate instruments with precision through VR controllers that closely mimic actual manual interactions. IM-MetaLAB supports both synchronous and asynchronous participation, allowing for flexible access and extended individual use of lab equipment, which is often limited in traditional settings. In addition to hands-on practice, the platform fosters teamwork and social interaction, helping to alleviate the sense of isolation commonly associated with remote learning. Integrated features such as shared wall displays and interactive dashboards further enrich the learning experience and help develop key skills such as collaboration and problem-solving.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".