Effect of basalt fiber and carbon fiber on the evolution of water evaporation and cracking characteristics of silt soil
Bibliographic record
Abstract
Cultivated land is prone to cracking during the high-temperature season, resulting in accelerated water evaporation, incomplete soil structure, and waste of resources caused by pollutants entering the soil. In this study, image analysis technology was used to compare the effects of two kinds of fibers on the drying and shrinkage characteristics of silt under the same volume. The results show that during cracking process, water evaporation can be divided into three phases: constant rate stage, deceleration rate stage, and residual stage. The crack rate of 0.08% basalt fiber-treated soil and 0.06% carbon fiber-treated soil is reduced by 27.66% and 27.98%, respectively. The addition of fibers acts like a bridge, narrowing the concentration of crack width from 0.2–0.8 to 0–0.6 mm, increasing short cracks of 0–5 mm and decreasing long cracks larger than 5 mm, thus reducing the soil cracking rate. With rough surface and high elastic modulus of fibers, the addition of fibers enhances the friction between soil particles to limit the movement of soil mass, and bears part of the tensile stress when cracking. It increases the tensile strength between the soil mass and effectively reduces the crack rate. It is found that the improvement effect of basalt fiber is relatively better and the economic benefit is higher under the same volume.
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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".