Monitoring the Dynamic Vertical Clearance under the Laviolette Bridge on the St. Lawrence River
Bibliographic record
Abstract
Internationally, the size of vessels keeps increasing. This causes a problem for the merchant navy, as many obstacles can limit the passage of larger vessels. When a ship sets sail for the Port of Montreal, the Laviolette Bridge near Trois-Rivières is the main aerial obstacle. It is why the Montreal Port Authority with Québec’s Ministry of Transportation’s authorization and collaboration launched the Laviolette Bridge monitoring project. The purpose of this monitoring was to analyze the variation of the vertical clearance under the bridge for a year. To achieve this, four global navigation satellite system (GNSS) receivers/antennas, a laser rangefinder, a radar rangefinder, and a weather station were installed on the bridge. These instruments helped to quantify the bridge’s movement as driven by factors such as wind, temperature, and traffic, as well as their impact on the vertical clearance. The results presented in this paper show that the temperature difference between winter and summer causes altimetric variations of up to 6 cm at the top of the bridge and 3 cm at the deck level. The water level fluctuations of the St. Lawrence River are by far the most significant factor. It varies up to approximately 3 m at the location of the Laviolette bridge due to seasonal fluctuations. Two independent vertical clearance models were developed and compared. The first one considers that the bridge has a fixed height and that only the water level varies. The second model uses the radar rangefinder installed under the bridge to measure vertical clearances that account for the movements of the bridge. In general, the two models agree within a few cm, and this difference slightly varies according to the seasons. By applying a thermal correction to the first model, the gap between the two models is reduced.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".