Advanced Decision Support in the Ontario Bridge Management System
Bibliographic record
Abstract
The Ministry of Transportation of Ontario (MTO) has engaged Stantec Consulting, Ltd. to develop a new state-of-the-art Bridge Management System to support the management of the Province's 3000 bridges. Developed in Visual Basic, the software will exploit object technology to produce a practical decision support system that recognizes the complexities of bridge management. A bridge management system (BMS) supports policy and programming decisions by predicting the engineering and economic outcomes that may result from those decisions. To do this, a BMS incorporates deterioration models, cost models, business rules for treatment selection and costing, and an analytical framework for calculating and presenting information relevant to the decision at hand. In Ontario, nearly all project-level decision-making in bridge management is performed by structural engineers, based in MTO's five regions, which conduct biennial inspections and plan future work. The new BMS is therefore designed to satisfy the decision support needs of these engineers, by providing life-cycle cost and tradeoff information in the field while the engineer visits each bridge for inspections. This paper describes the engineering models used in project-level analysis.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".