Using Cloud-Computing to Promote Asset Management Best Practices - A Ministry of Transportation Ontario Case Study
Bibliographic record
Abstract
For many years, transportation agencies across Canada have been collecting roadway asset inventory and asset condition datasets across their networks with the aim of facilitating best practice in maintaining these assets at the highest levels of service and within budget. However these extensive datasets are often managed by only a few individuals responsible for developing high level work programs for the upcoming or future construction seasons. With the advent of cloud computing, and its further development in recent years, a new interactive medium is now available for all agency staff, contractors, consultants, and end users to gain on-demand access to this highly valuable dataset, current and historic roadway asset attributes and conditions, in addition to roadway imagery. To demonstrate the many applications and benefits of a web-based medium for accessing these datasets and imagery, the Ministry of Transportation of Ontario’s use of iVision is presented as a case study. Feedback received from key individuals in MTO during all phases of implementation of this web-based application and the statistics obtained through monitoring provide insight that other Canadian, State and Municipal agencies can benefit from. This paper explains how an accessible and interactive web application can promote best asset management practices for planning and day-to-day operations of roadway networks.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".