Compressive strength of soils amended with a bacterial succinoglycan: effects of soluble salts and organic matter
Bibliographic record
Abstract
The ability to stabilize soils in a cost effective and efficient manner has utility in both civil and military applications. This study examines the ability of a bacterial succinoglycan to bind and strengthen the silt fractions of three geochemically different surface soils. Small-scale specimen preparation and uniaxial compression test methods were developed to observe effects of biopolymer concentration and silt surface condition on specimen stress–strain response. Results indicate that the biopolymer was effective at strengthening all three natural silts and when applied at concentrations of 1–15 mg·mL−1, the increase in strength was linear. Silt surface condition was then modified by sequential removal of soluble salts and organic matter. For two of the silts, the removal of salts and organic matter had significant and cumulative negative effects on specimen compressive strength, deformation at peak stress, time to failure, and absorbed strain energy at failure. For a silt characterized by a high cation exchange capacity, high pH, and low aggregate percentage, the removal of organic matter did not reduce compressive strength beyond the level associated with the removal of soluble salts. Results from this study indicate that surface modification can significantly affect the compressive strength of silt materials and identified aggregate content as a principal determinant.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".