Restoring The Permeability Of Peat Soil Using Sand-Mixed And Bio-Grouting Techniques Made From Bacteria
Bibliographic record
Abstract
Restoring the permeability of peat soil using a mixture of sand and bio-grouting techniques made from bacteria aimed to determine the effect of adding 5% sand by weight of dry soil and bacteria on the permeability of peat soil. Peat soil is a problem that is often encountered in the world of construction. Peat is a type of soil formed from the accumulation of half-decayed plant remains which causes it to have a high organic matter content. The research testing method refers to ASTM (American Society for Testing and Materials) procedures and SNI. Preliminary testing, namely testing the original soil moisture content, testing the original soil specific gravity, and testing standard compaction (proctor test). Meanwhile, permeability testing was carried out using the falling head method. The original soil was taken at the location of Buana Makmur Village km 55, Dayun Sub District, Siak Regency, namely in the form of sand 5% of the dry weight of the soil, additional ingredients in the form of bacteria and CaCl2 and Uruea. Based on the results of testing the physical properties of the original soil, it is known that the soil in Buana Makmur Village, km 55, Dayun Sub District, Siak Regency, is an original soil type with a water content of 407.5% and a specific weight (Gs) of 1.3 gr. Based on the results of the permeability test conducted, mixing 5% sand and adding 25% bacteria had the lowest permeability coefficient value compared to the original soil and sand mixed soil which had a permeability coefficient of K20oc = 1.166x10-3cm/second.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".