virtual and physical environment
Bibliographic record
Abstract
Nowadays, industrial software and communication tools allow the managerial staff, engineers and technicians to enhance the productivity and optimize automated production systems without the necessity of being physically on the production floor. In some instances, equipment providers can even assist plant engineers and technicians in troubleshooting specific equipments from a distant site, using advanced tools providing real-time information (visual, process data, etc.) from the plant. The current project, through the use of recent industrial communication technologies, proposes the reproduction of a similar situation, in joint learning activities for future engineers and technicians. This kind of interaction, in an early educational context at both levels, is unique to the knowledge of the authors. The institutions taking part in this project are the Université du Québec à Rimouski (UQAR) and the Cégep de Rivière-du-Loup, located in eastern Quebec. Two activities were initiated, the first as part of a process control course using a stand-alone physical setup, and the second using a currently designed mini-plant for recycling beverage bottles and cans as part of a sequential automation course. Results showed that the students at both institutions were able to work together and communicate effectively despite their different background. The training objectives for this phase of the project were successfully achieved and lessons learned will be further exploited to enhance the training level and student-acquired skills during the following phase of the project.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".