Transportation Association of Canada
Bibliographic record
Abstract
The theme for the 2006 Transportation Association of Canada (TAC) Annual Conference, “Transportation without Boundaries ” creates an image in the minds of most transportation professionals of what we would like to achieve within the next decade. However, our pragmatic side says there are institutional barriers that need to be breached and entrenched work cultures that must be changed in order to attain such a vision. A computer-based training (CBT) program is helping transportation agencies in Canada and the United States (US) transcend traditional training boundaries of time and distance to improve winter maintenance operations. The CBT is being used in both individual and conventional group training modes. It is most effective and efficient as an individual self-paced instruction program. Both the group and individual modes are structured to serve a wide range of learning abilities in the student population, including those with challenges such as dyslexia or hearing loss. In an effort to reach field forces more effectively, the American Association of State Highway and Transportation Officials (AASHTO) launched an Anti-Icing/Road Weather Information System (AI/RWIS) CBT into the winter maintenance training arena. Over 90 % of US snow-belt states, three provinces and numerous cities in both countries have purchased the CBT. This paper describes how the AI/RWIS CBT is being utilized in both countries and underscores the valuable lessons learned from successful and not-so-successful deployments. It also shows how institutional barriers were breached, entrenched work cultures changed, and how recent research is being incorporated into the instructional content to ensure that training material stays current. 3
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.521 | 0.268 |
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".