Effect of urease enrichment degree of multiple sources of urease on bio-cementation efficacy via enzyme-induced carbonate precipitation
Bibliographic record
Abstract
This study aims to investigate the effect of the urease enrichment degree of high-purity commercial urease and plant-derived crude ureases from sword bean (SWCU), soybean (SCU), and pigeon pea (PCU) on bio-cementation efficacy via enzyme-induced carbonate precipitation. The urease enrichment degree is defined as the urease activity per gram of organic matter in the urease solution. Bio-cementation efficacy was evaluated by the distribution and morphology of the precipitates and unconfined compressive strength. The results show that the urease enrichment degrees of the high-purity commercial urease, SWCU, PCU, and SCU are 1.08, 1.05, 0.57, and 0.31 mmol/min/g, respectively. The urease enrichment degree is the dominant factor influencing the pattern of CaCO3 distribution by affecting the organic matter distribution. The high-purity commercial urease-treated sand has the smallest calcite crystals (13–23 µm) and the lowest strength (172 kPa). For the plant-derived crude urease, with the decrease of the urease enrichment degree, the generated crystals become smaller, the CaCO3 distribution becomes less uniform, and the soil strength decreases. The SWCU-treated sand exhibits the best bio-cementation efficacy and is recommended to enhance soil strength.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".