Atmospheric Corrosion of Steel Infrastructure in Canada Under Climate Change
Bibliographic record
Abstract
Protection against atmospheric corrosion of steel infrastructure and its components requires assessment and prediction of corrosion rates using quantitative and reliable modeling of the environmental parameters that impact the corrosion process. This has become even more critical in the context of climate change. This study demonstrates the feasibility of using ISO dose-response functions (DRFs) to classify atmospheric corrosiveness in Canada to improve the current qualitative approach to the corrosion design of carbon steel infrastructure, especially bridges. The DRF predictions for first-year corrosion rates were first validated using a set of historical field data collected from different locations across Canada. Further examination has revealed a dramatic change in atmospheric corrosiveness at many locations in Canada since the last systematic effort in field data collection in the 1950s to 1960s. It is believed that this was caused by a significant drop in in sulfur dioxide levels in the atmosphere. A projection of future corrosiveness in various Canadian cities with differing climates was also presented by considering the projected impacts of climate change on the temperature and relative humidity. This study has shown a promising precursor for the development of a corrosiveness map in Canada to better guide the corrosion design and protection of steel infrastructure. Furthermore, this study has demonstrated an acute need to understand the current chloride deposition conditions of steel bridges, including the increased local exposure of roadways to corrosiveness by the use of de-icing salts.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".