Property changes of reactive magnesia–stabilized soil subjected to forced carbonation
Bibliographic record
Abstract
A reactive magnesia (MgO) was used to stabilize a natural soil; the MgO-stabilized soil was subjected to forced carbonation with pressurized gaseous CO2 in a triaxial cell set-up. The change of physical properties, including bulk density, moisture content, dry density, specific gravity, and porosity, of the stabilized soil during carbonation was studied. The mechanical and microstructural properties of the carbonated MgO-stabilized soil were also investigated through unconfined compressive strength (UCS) test, X-ray diffraction analysis (XRD), scanning electron microscopy (SEM), and mercury intrusion porosimetry (MIP). The results indicated that the carbonation of MgO-stabilized soil consumed CO2 and water, and produced expansive carbonation products; this consequently increased the dry density, and reduced the moisture content, specific gravity, and porosity of the stabilized soil. After being carbonated for only 1.5 h, the MgO-stabilized soil yielded remarkable strength, with UCS higher than that of the 28 day ambient cured Portland cement–stabilized soil, mainly due to the high binding effect of carbonation products and the low porosity of carbonated MgO-stabilized soil. The carbonated MgO-stabilized soil achieved a high degree of carbonation in a few hours (≤12 h), with the maximum CO2/MgO ratio in a range of 0.76–1.07.
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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".