The influence of Interground limestone fines and metakaolin on the electrical resistivity of portland-limestone concrete
Bibliographic record
Abstract
The critical climate change has raised concern about the decarbonization of the cement and concrete industry, which is responsible for 8% of global CO2 emissions. Portland limestone cement (PLC), which is made by partially replacing the clinker with up to 15% interground limestone fines (LFs), has been recognized as a viable solution for its feasibility to match the engineering properties of ordinary portland cement (OPC). However, with the necessity of further increasing the LFs contents to meet the desired eco-efficiency, the dilution effect brought by less ultimate hydration products may be detrimental to the long-term performance of reinforced concrete structures, such as chloride-induced corrosion. Thus, this research explores the potential of combining PLC and alumina-rich supplementary cementitious materials (SCMs) to improve the resistivity of the concrete. Concrete specimens were fabricated with PLCs from two sources of three LFs replacement ratios (15, 20, and 25%). MK (8%) is used as the source of alumina. The bulk and surface resistivity results showed that combining PLC with MK can notably improve concrete resistivity even in mixtures with lower amounts of cement. Additionally, compressive strength demonstrated poor correlation with electrical resistivity, which highlights the significance of performance-based 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".