Bibliographic record
Abstract
Many of the traditional clients in the public sector, including transportation and power authorities, have gradually shifted away from production areas to concentrate on direct project delivery. This has led to a large vacuum in the research and development areas, leaving engineers in the private sector to fill this role. At the same time, the ability of engineers in the private sector to design and innovate was also severely limited. The group of engineers most affected are bridge engineers. Major clients such as the Ministry of Transportation of Ontario (MTO), has outsourced their bridge design almost entirely since the mid 1990's. Because of the lack of knowledge and experience in designing bridges within MTO, its engineers have limited ability in in-house designs and evaluations, as well as in consultant management. In early 2012, MTO embarked on a new initiative to create and implement a Training Program for Bridge Engineering based on the Canadian Highway Bridge Design Code (CHBDC). The program is to address this knowledge gap, allow MTO to undertake in-house design activities, maintain MTO as a knowledgeable owner, and enhance its ability to oversee Bridge Design Consultants. Morrison Hershfield Limited and the University of Western Ontario rose up to this challenge. The resulting program combined expertise in practical bridge design with academic teaching skill in adult education. The program was successfully delivered, with the work monitored by a Steering Committee comprised of MTO Senior Members. This project was nominated for the 2013 Educational Achievement Award. For the covering abstract of this conference see ITRD record number 201310RT334E.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.074 | 0.010 |
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".