Climate change-induced carbonation of concrete infrastructure
Bibliographic record
Abstract
There is a nearly unanimous consensus among scientists that increasing greenhouse gas emissions, primarily carbon dioxide generated by human activity, are effecting the Earth's climate. For many key parameters, the climate system is already moving beyond the patterns of natural variability within which our societies and economies have developed and thrived. These parameters include global mean surface temperature, sea-level, ocean and ice sheet dynamics, and extreme climatic events. There is a significant risk that many of the trends will accelerate, leading to an increasing risk of abrupt or irreversible climatic shifts. One overlooked area of research is the impact of climate change on concrete infrastructure. Concrete structures form an essential part of the world. Climate change could potentially affect the durability of concrete infrastructure. In this paper, the findings of a study at the University of British Columbia which demonstrate a long-term risk to the durability of steel-reinforced concrete structures in some parts of the world via accelerated carbonation-induced corrosion are presented. Research demonstrates that service lifespans of structures will be affected in the long term, with the need for earlier repairs unless the adverse effects of climate change are taken into account at the time of design.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".