Assessing Highway Bridge Chloride Exposure at a Provincial Scale: Mapping and Projecting Impacts of Climate Change
Bibliographic record
Abstract
This study addresses the absence of a provincial scale database on chloride exposure for bridges, which specifically focuses on Ontario, Canada. Considering the region-based climate and traffic characteristics, a comprehensive database was developed based on chloride exposure prediction models. This database documents chloride exposure information under different Representative Concentration Pathways (RCPs) of greenhouse gas (GHG) emissions for bridge piers and decks across regions and times. This prediction model was validated against experimentally derived chloride profiles from concrete core samples. The database allows for the assessment of spatial and temporal patterns in chloride exposure, which considers the projected climate change conditions. To facilitate data interpretation, this study employs heat maps to transform the extensive provincial database into a visually accessible format. Bridge piers in Southern Ontario have higher surface chloride concentrations than those in Northern Ontario; however, bridge decks in Northern Ontario tend to exhibit higher surface chloride concentrations compared with those in Southern Ontario. Future projections suggest an overall increase in the surface chloride concentrations on bridge piers across most of Ontario under the RCP4.5 and RCP8.5 scenarios, which is largely attributed to increased traffic volume. However, certain southern regions might see a decrease under RCP8.5, which is a direct consequence of climate change. In contrast, surface chloride concentrations on bridge decks are expected to decline, a trend that is driven by diminishing snowfall and increasing traffic volume. In addition, these findings suggest that relying on RCP8.5 could lead to underestimating the climate change impacts compared with RCP4.5. This study highlighted the spatiotemporal patterns in highway bridge chloride exposure in the context of climate change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".