Bibliographic record
Abstract
This thesis presents a study of Mechanical Engineering skills as they are related to the needs of Canadian industry. Current Mechanical Engineering curricula in accredited engineering programs in Canada have been compared to the needs expressed by Canadian industry. A list of 70 Best Practices were defined to be part of the Product Engineering Process (PEP). The PEP is the process made up of "Best Practices", used to develop a product from idea or concept to how to dispose of or recycle the product at the end of its useful life. Two surveys have been constructed, one for academia and another for industry. These surveys were used to collect the appropriate data from each group. The surveys contain a demographic page, which asks specific questions to the participant. Pages will follow which list 70 elements found in the PEP. The skills are divided into eight categories, they are: (1) knowledge of PEP; (2) PEP team skills; (3) people skills; (4) professional communication skills; (5) design skills; (6) analysis and testing skills; (7) manufacturing skills; (8) connection with the global marketplace. (Abstract shortened by UMI.)Dept. of Mechanical, Automotive, and Materials Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .S57. Source: Masters Abstracts International, Volume: 40-03, page: 0775. Adviser: Peter R. Frise. Thesis (M.A.Sc.)--University of Windsor (Canada), 2000.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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".