Effective Infrastructure Management Solutions Using the Analytic Hierarchy Process and Municipal DataWorks (MDW)
Bibliographic record
Abstract
The economic success of a nation, province, or municipality is closely tied to the condition of their civil infrastructure. Ontario roads and bridges built throughout the 1950s and 60s are quickly approaching the end of their design life. Without the necessary funding, the declining condition of these assets can result in roads with compromised safety and increased road user cost. Historically, there has been an underinvestment in maintaining our infrastructure at an acceptable level. Small municipalities are the most seriously affected by this underinvestment and are continually placed in a position where they are required to do more with less. In 2008, the Ontario provincial government, the Association of Municipalities of Ontario (AMO), and the City of Toronto published the Provincial-Municipal Fiscal and Service Delivery Review that identified a $60 billion investment was required over 10 years to bring our aging physical structures and facilities back to an acceptable standard. Of this amount, $28 billion would need to be dedicated to upgrading Ontario's roads and bridges. The paper focuses on how small municipalities can effectively manage and improve the condition of their aging road infrastructure based on several funding scenarios. To achieve this, best practices in the area of infrastructure management and capital investment planning will be explored using the Ontario Good Roads Association's asset management software, Municipal DataWorks (MDW). (A) For the covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".