Updated skeleton void ratio for gravelly sand mixtures considering effect of grain-size distribution
Bibliographic record
Abstract
Recent research on the behavior of gravelly sands advocate for the use of the skeleton void ratio to characterize their density state. The skeleton void ratio corresponds to the void ratio of grains constituting the stress-bearing skeleton. However, such a void ratio relies on parameters difficult to determine in practice, such as the fraction of fine grains that take part actively in the load bearing skeleton. Also, it fails to consider the effect of grain-size distribution (GSD) of gravel and sand grains. Therefore, the skeleton void ratio index introduced by Chang et al. as published in 2015 is revisited to account for the effect of GSD of both gravel and sand grains. Two semi-empirical equations are developed in this paper to connect GSD parameters with skeleton void ratio parameters. The validity of the proposed equations has been checked for a particular class of gravelly sand materials. A series of specially designed drained triaxial tests on gravelly sands were then conducted. Test results show that it is essential to consider the effect of GSD when using the skeleton void ratio index. It also verifies the effectiveness and applicability of the proposed updated skeleton void ratio, which shows advantages in characterizing critical state lines of gravelly sands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".