Role of substrate roughness in soil desiccation cracking
Bibliographic record
Abstract
Soil desiccation crack is ubiquitous in nature, yet the physics of its initiation and propagation remain under debate, as it involves complex interactions across multiple fields of mechanics, hydraulics, and thermals. Here, an experimental attempt is made to uncover the role of substrate roughness on the soil desiccation process. The substrate roughness is deliberately fabricated by 3D printing, whereas the thickness of sample and environmental humidity are controlled to eliminate the effect of large hydraulic gradient. Four types of soils with varying expansibilities were desiccated on substrates with varying roughness. It reveals that (1) soil desiccation crack evolution can be conceived as a competing process between the shear failure of soil–substrate interface, i.e., slippage of interface, and the tensile failure of soil, i.e., crack initiation, in minimizing the total energy of drying soil; (2) substrate roughness alters the failure mode and shear strength of soil–substrate interface and its sensitivity to moisture, thereby it regulates the pattern of how soil crack propagates upon drying; and (3) soil expansibility is recognized as a key factor governing the crack initiation in addition to the widely recognized air entry, and flaws in soil are the sources for the 120° crack angle and bimodal crack angle distribution.
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 | 0.000 |
| 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".