CAN-Bus Remote Laboratory on WebLab-Deusto System
Bibliographic record
Abstract
Technologies of online education, such as videoconferencing, online learning management systems, and remote laboratories play crucial role in modern world, providing equal possibilities for students all over the world.The goal of the paper is to present the results of the development of a remote laboratory for online access to the CAN-Bus experiment for students of electrical engineering programs.The developed remote laboratory setup is based on Remote Laboratory Management System WebLab-Deusto, and extends existing in Bonn-Rhein-Sieg University of Applied Sciences CAN-Bus experiment.The laboratory consists of several layers, which are described in detail in the paper -layer of management system (server with WebLab-Deusto instance), an experiment server based on Raspberry Pi, input-output infrastructure for the connection with the experiment, and finally, the existing experiment equipment.The Python-based web framework Flask and library Weblablib were used in the experiment server, providing a remote laboratory web application and human-machine interface for the interaction with the equipment.The laboratory can be used in remote and on-site modes and can be further integrated to the university's learning management system Moodle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.013 |
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".