Seawater based bio-cementation for calcareous sand improvement in marine environment
Bibliographic record
Abstract
Seawater based bio-cementation through microbially-induced carbonate precipitation was proposed for the calcareous sand improvement in marine environment. The method used seawater instead of traditional fresh water to culture urease producing-bacteria (UPB) and prepare cementation solution (CS) for the bio-cement. A series of comparative bio-treatment tests using seawater based bio-cementation and traditional bio-cementation methods on three types of soil were conducted. Experimental results indicate that seawater based bio-cementation method has the ability to improve soil physico-mechanical properties, and performed better than traditional bio-cementation method. The dominant reason can be explained as that the mixture of the productions of calcite, monohydrocalcite and calcite magnesium produced during seawater based bio-cementation process have better cementation ability than the mixture of the productions of calcite and vaterite produced during bio-cementation process. UCS of coarse Ottawa sand blocks are smaller than that of medium Ottawa sand blocks is because the specific surface area of fine sand is higher and larger number of effective bondings can be formed. UCS of calcareous sand blocks are smaller than those of coarse Ottawa sand blocks can be attributed to the fact that calcareous sand has higher porosity and rougher surface, resulting in more carbonate crystals being precipitated on un-connected locations.
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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) | 0.000 | 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".