Accelerated carbonation of non-plastic soils mixed with hydrated lime: index properties influencing binder formation rate and mechanical improvement
Bibliographic record
Abstract
Chemical stabilization—the mixing of additives like cement, lime, or fly-ash with soil to improve its mechanical properties—conventionally relies on hydration reactions to generate a binder. Accelerated soil carbonation is a nascent alternative method, whereby carbon dioxide is intentionally introduced in soil mixed with alkali additives to generate a carbonate binder and sequester carbon dioxide. Non-plastic sand and silt specimens mixed with hydrated lime were carbonated for varying amounts of time at different water contents and densities to evaluate the index properties influencing the rate of carbonation and degree of mechanical improvement. It was demonstrated that volumetric air and water contents primarily govern the rate of binder formation and that mechanical properties are influenced by the carbonate binder content and density. Under optimum conditions, soil specimens could be fully carbonated within 3–24 h and unconfined compressive strengths as great as 3–4 MPa were achieved. The degree of strength improvement is comparable to cement-stabilized materials and has a similar dependence on soil type, density, and binder content. If techniques are developed that enable carbonation at scale, the sequestration of carbon dioxide would offset emissions associated with production of chemical additives used for chemical stabilization.
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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".