Exploring the effect of biopolymers in near-surface soils using xanthan gum–modified sand under shear
Bibliographic record
Abstract
Biopolymers produced in near-surface soils by living organisms, including microbial extracellular polymeric substances and plant mucilage, offer enhanced moisture retention and protection from dry environments, lubricate roots to allow penetration through soil, and link soil grains together physically to form soil aggregates. At the aggregate scale their effects and behaviour are known and significant, but their impact on geotechnical behaviour of shallow soil bodies at the mesoscale and beyond is largely unexplored, including their response to the moisture cycling typical in vadose zone soils. In this work we explore the effects of moisture conditions, including multiple dry–wet cycles, on the shear behaviour of sand amended with xanthan gum as a model biopolymer. Drying causes a significant improvement on shear strength, even at low concentrations of biopolymer, but this is largely lost upon wetting. The extent of shear strength improvement is dependent on the moisture path taken (i.e., the wetting–drying history) and deteriorates over a number of moisture cycles. We present a conceptual model that poses redistribution of the biopolymer around the sand grains as the cause of the observed behaviour and demonstrate that biopolymers can provide a significant although transient enhancement of shear strength of sand in near-surface conditions.
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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) | 0.001 | 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".