Correlations between porosity, chloride diffusivity and electrical resistivity in volcanic pumice-based blended cement pastes
Bibliographic record
Abstract
This paper reports the results of an investigation on the chloride diffusivity of volcanic pumice (VP)-blended hardened cement pastes with varying curing age of up to 1 year. The pastes had 0, 20 and 40% VP as cement replacement and water to binder ratios (W/B) of 0·40, 0·50 and 0·60. The accelerated chloride ion diffusion test was used to calculate the chloride ion diffusion coefficient (Di) of pastes using the Nernst–Plank equation for steady-state condition. In addition, electrical resistivity, mercury intrusion porosimetry, and differential scanning calorimetry (DSC) tests were also conducted. Good correlations were found between Di, total pore volume and electrical resistivity of the pastes. The Di values of VP-blended pastes were within the range of 10−7–10−9 cm2/s. It was also found that blending cement with VP significantly reduced the long-term chloride ion diffusion coefficient and hence increased the long-term corrosion resistance of pastes. This fact was also supported by the presence of lower quantity of Ca(OH)2 and higher quantity of Friedel's salt in the VP-blended pastes as observed from the DSC tests. Pastes with 40% VP showed better performance in terms of chloride ion diffusivity. The water to binder ratio was also found to affect the Di at the early ages of curing, but became less important at the later ages of curing.
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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 | 0.000 |
| 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) | 0.000 | 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".