Bibliographic record
Abstract
The University of Ottawa faculty of engineering, in Ottawa Canada, is home to multiple rapid prototyping facilities as well as entrepreneurship spaces. This includes a makerspace, a machine shop and a design space for any student to use free of charge. Due to COVID-19 the spaces were either shut down or running virtual activities where possible. In the absence of any significant virtual content for learners, virtual computer simulations and virtual reality simulations were developed for various technologies including a manual mill and lathe, a laser cutter and soldering. Even as the COVID-19 restrictions are being lifted, the virtual simulations will be used as a pre-training introduction for in-person sessions. This paper aims to understand how well the virtual training simulations compare and compliment the in-person training for different equipment. Factors considered are the level of previous knowledge and level of interest in the equipment. The same assessment will be given to 3 groups of participants: those who have only done the virtual training, who have only done the in-person training and who have done both. The results from each group will be compared and analyzed to determine the efficacy of the virtual simulation and what advantages it has as a pre-training resource.
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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".