Vulnerability of network infrastructure due to potential extreme weather in PEI, Canada
Bibliographic record
Abstract
The Prince Edward Island Department of Transportation and Infrastructure (DTI) is responsible for the densest network of roadway and bridges per capita of any of the Canadian Provinces. DTI own and manage a variety of structures, with about 99.5% of which cross some sort of waterway (a creek, stream or river). Of the water crossing structures, approximately 11% of these are coastal in nature. Over the last 16 years, DTI has adopted bridge management processes including implementing an advanced bridge management system (BMS). This has enabled the department to make significant improvements in the management of inventory and inspection data, the inspection process, tracking and reporting condition through a standard condition index, performing asset valuation, and in handling of inspection photos, videos, plans and other documents. Recently, as a result of increased extreme weather events, there have been growing concerns related to the risks to our infrastructure and infrastructure resiliency. Rising sea levels are affecting the PEI coastline with estimated erosion of 30 cm per year and potentially affecting tidal flow in coastal rivers. Storm intensities are increasing; PEI was battered by Post Tropical Storm Fiona in September of 2022, which, among other destruction, downed several hundred hectares of woods in our province which still lays there today. In Spring 2023, wildfires occurred in many provinces across Canada, including several thousands of hectares in neighboring Nova Scotia. DTI, in conjunction with Stantec, are implementing appraisal indices to include the effects of extreme events on flooding, scour, wildfires and other vulnerabilities and providing useful risk analysis and a measure of resiliency. This paper will highlight how the province is adapting our BMS to account for potential extreme weather-related events, such as flooding, scour, and wildfires. The paper will be of interest to other jurisdictions who manage bridges and culverts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".