A Tale of Two Bridges: Extending the Lifetimes of the Lions Gate and Angus L. Macdonald Suspension Bridges
Bibliographic record
Abstract
This paper examines similarities and differences between how the Lions Gate Bridge (LGB, opened in 1938 in Vancouver, British Columbia, Canada) and the Angus L. Macdonald Bridge (ALM, opened in 1955 in Halifax, Nova Scotia, Canada) were renovated and upgraded to address structural safety concerns and rapidly increasing maintenance costs. In both cases, parallel replacement bridges were not an option due to urbanization and/or property issues both ends of the bridges. In addition, neither bridge could be closed for long durations due to a lack of excess capacity in the local transportation network. As such, both bridges had to remain on their existing alignments while undergoing extensive rehabilitation and maintaining weekday daytime traffic. In both cases, superstructure replacement schemes were developed that allowed the superstructure to be sequentially replaced in segments during short night-time or weekend closures.This paper briefly describes the design features of both bridges, the scopes of work, similarities and differences between the erection works on both bridges, and the significant environmental differences between the two bridge sites. Importantly, lessons learned on LGB are noted, and how they were applied to ALM. Finally, the paper discusses the lessons learned on ALM and their applicability to future projects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".