Engineering Science - Infrastructure Engineering - University of Toronto
Bibliographic record
Abstract
University of Toronto's Infrastructure Engineering Option in Engineering Science provides the most comprehensive undergraduate program in transportation engineering in North America, and is in our view an ideal candidate for the TAC Educational Achievement Award. Infrastructure Engineering is one of eight options that define the final two years in the four?year Engineering Science Program. Within Infrastructure Engineering, transportation engineering is offered as one of two specialties (the other being structural engineering). Students that select the transportation specialty take ten transportation engineering courses, conduct a single term thesis in transportation engineering, and take courses in design, structural engineering, project finance and management to complement the transportation methods and applications learned in the other courses. To our knowledge, this is the most comprehensive undergraduate program in transportation engineering in Canada. The program is highly innovative in its approach to teaching the discipline of transportation engineering, with a firm grounding in fundamentals of science, opportunities for research, and a wide range of opportunities for application and design that has prepared students to make a significant contribution to better transportation engineering in Canada. This program was nominated for the TAC 2010 Educational Achievement Award. For the covering abstract of this conference see record control number 201111RT334E.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.343 | 0.105 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".