A new index to measure the uniformity of remolded loess
Bibliographic record
Abstract
Abstract The uniformity of remolded loess is crucial for engineering stability and in laboratory testing, as it affects physical and mechanical properties. It is important to have an index which can accurately and conveniently evaluate the uniformity of remolded loess. This study demonstrated and verified the feasibility of using hydraulic conductivity (K) as an indicator for evaluating the uniformity of remolded loess through laboratory experiments and theoretical analysis. In laboratory research, nine loess samples under different preparation conditions were meticulously prepared in duplicate, which were divided into three sets according to the whole dry density (WDD) of approximately 1.3 g/cm3, 1.4 g/cm3, and 1.5 g/cm3 respectively. For the nine duplicate samples, two procedures were performed for each of the sample. One is the uniformity analysis by cutting the soil column and weighing. The other is the hydraulic conductivity experiment. Results showed that sample uniformity is affected by sample preparation conditions, and there are differences in the uniformity of the same WDD samples. The values of K positively correlate with the degree of sample uniformity. In theoretical analysis, based on Darcy’s Law and Kozeny-Carman equation, it is found K is inversely proportional to the variance ( $$\sigma^{2}$$ σ 2 ) of the sample dry density. That is, K is positively proportional to the sample uniformity. Since K can be easily determined in the laboratory, the application of this new index in the field of geotechnical engineering makes it very convenient and simple to evaluate the uniformity of remolded loess.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".