What Do I Have and Where is it Located? Quantifying Ontario’s Municipal Lane Kilometres
Bibliographic record
Abstract
An essential requirement of a good asset management plan is data. There are many benefits to having good data: trust, reduced liability, improved asset knowledge, improved budgeting, and improved customer service. Without this, it is impossible to make strategic asset management decisions. In 1995, the Ministry of Transportation of Ontario ended the Conditional Grant Program that provided partial funding for municipalities to support maintenance, rehabilitation, and reconstruction of their roadways. This decision also impacted the collection of road inventory, condition and performance data. At this time, it was estimated that Ontario municipalities owned and maintained approximately 275,000 lane kilometres of road. Since this time, there have been numerous changes within the province that would affect the lane kilometre value: downloading of provincial highways to local municipalities; municipal amalgamations; and system growth/development. In an attempt to recapture some of this missing road infrastructure data, the Municipal Performance Measure Program (MPMP) was created in 2000. Under this program, Ontario municipalities are required to report efficiency and effectiveness performance measures for the services they are responsible for delivering as part of their Financial Information Return (FIR). Although mandated, there has never been 100% compliance by Ontario’s 444 municipalities. Fast forward to 2012, 343 Ontario municipalities (77%) submitted lane kilometre data through MPMP. Recognizing the importance of this value in an asset management context, the Ministry of Municipal Affairs and Housing (MMAH) initiated the Roads and Bridges Data Improvement Project. The goal of the project was to fill in the missing gaps and to create a complete data set for the number of Ontario lane kilometres that are under municipal jurisdiction and confirm the accuracy of the information being provided. Through rigorous follow-up with individual municipalities, MMAH was able to obtain 100% participation and determine that Ontario municipalities are responsible for 301,886 lane kilometres of road. The paper focuses on five key areas of the Roads and Bridge Improvement Project: context and goals; the data improvement process; projects results; data verification; and observations/lessons learned. The results of this effort is an accurate starting point to begin collecting important road infrastructure data that can be used to make strategic asset management decisions at both the provincial and municipal levels of government and allow for accurate benchmarking comparisons to take place.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".