Incorporation of Optical Density into the Blending Design for a Biocement Solution
Bibliographic record
Abstract
The engineering practices for applying the microbial precipitation of carbonates require a design of the blending biocement solution (BCS). The BCS is usually blended with concentrated strains NO-A10, reaction media, such as urea and CaCl2, and a solvent, i.e., water or seawater. To characterize the BCS, the unknown microbial characteristics, such as the cell viability, are complex factors. Therefore, the optical density (OD) was redefined as Rcv OD*, in which OD* was the tentative OD of the BCS used and Rcv was the conversion rate concerning the cell viability. To determine Rcv values, a standard precipitation curve based on the precipitation rate at 24 h was determined. It was found that the curve was expressed by λ1 OD+ λ2 OD2, in which λ1 and λ2 were 8.46 M and −17.633 M, respectively. With this, the Rcv and OD values of unknown BCS were estimated from the results of precipitation tests using arbitrary OD* values. By extending the testing time, the second order term of OD or OD* was negligible. Accordingly, the precipitation amount is expressed as 8.46 OD, in which the OD can be estimated by precipitation tests using arbitrary OD* values of BCSs. Unless the Ca2+ value is dominant, the optimum blending of BCS can be determined by OD. Thus, it is concluded that the blending design of BCS is achieved using 8.46 OD, or 8.46 Rcv OD*, and the standard precipitation curve was defined in this study.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".