UNIVERSITY-INDUSTRY COLLABORATION IN MULTIDISCIPLINARY DESIGN EDUCATION: THE SANDVIK MINING EQUIPMENT - QUEEN'S MINE-MECHANICAL EXPERIENCE
Bibliographic record
Abstract
Queen’s University at Kingston has been graduating a unique breed of multidisciplinary engineer since 1994: the Mine-Mechanical option students within the Queen’s Mining program are exposed to the fundamentals of both Mechanical Engineering and Mining Engineering. The final year capstone engineering design project in the Mine-Mechanical option focuses on mining equipment design, and since 2000 this multidisciplinary project has been carried out in collaboration with Sandvik Mining and Construction of Burlington, Ontario. The students work on real world design projects formulated by design engineers at Sandvik, under close communication and coordination with academic project advisors. These design projects are differentiated from typical mechanical engineering design projects in that they require a thorough understanding of the mining context in which the equipment is to be deployed and operated. This paper will present the structure and format of this university-industry educational collaboration, review past successes, evaluate the educational outcomes as well as benefits to industry, and ponder some lessons learnt.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".