A pavement maintenance management system designed for the city of W nnipeg
Bibliographic record
Abstract
A large portion of the regional street network in Winnipeg is comprised of Portland Cement Concrete pavements--pavements which are susceptible to damage caused by freeze-thaw cycles. Due to the high number of freeze-thaw cycles in Winnipeg, pavement maintenance is therefore important. The goal of this project is to develop a method of selecting effective and efficient pavement maintenance strategies. There are two elements of pavement maintenance management, (1) reactive, (2) proactive. The reactive method proposed in this report invokes using a maintenance activity assignment procedure that is dependent on human experience, to develop rules which are used to assign maintenance treatments to pavements based on condition. The proactive technique proposed involves assessing the probability that the pavement will deteriorate one condition category in one analysis period, and recommending a long-term maintenance strategy based on that probability and background information. Once the long-term strategy is recommended, the individual life-cycle maintenance strategies for each pavement are developed. Using a form of life-cycle cost analysis, the lowest cost alternatives are chosen. Once all of the pavements being considered have a life-cycle maintenance strategy then the pavements are ranked in order of importance. This is done so that the highest ranking pavements are funded until the budget is exhausted. Maintenance on lower-ranking projects is deferred until sufficient funding can be obtained. The system depends on a level of funding that has not been available to maintenance engineers and planners in recent years.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".