Bibliographic record
Abstract
Mixtures of small rigid sand particles Dsand large soft rubber particles Drare prepared at different volume fractions and tested to investigate their small-strain and zero-lateral strain responses (Dr /Ds≈ 10). Both data sets are simultaneously gathered in an oedometer cell instrumented with bender elements. Data are analyzed in the context of mixture theory and with the aid of numerical simulations. Results show that the sand skeleton controls the mixture response when the volume fraction of rubber particles is Vrubber ≤ 0.3, while the rubber skeleton prevails at Vrubber ≥ 0.6. The large size and incompressibility of rubber particles provides high stress-induced stiffness in the sand skeleton near the equatorial plane of rubber particles. The corresponding increase in local small-strain shear modulus Gmaxresults in earlier wave arrivals in mixtures with Vrubber ≤ 0.3 than in pure sand, while the quasi-static constrained modulus is highest in pure sand. The constrained modulus and shear wave velocity are power functions of the applied effective stress in all mixtures. Results from this study (Dr /Ds≈ 10) and from a previous complementary study with small rubber particles (Dr /Ds= 0.25) show that the development of internal fabric, particle level processes, and the associated macroscale response of sand–rubber mixtures depend on the relative size between the soft rubber chips and the stiff sand particles Dr /Dsand their volume fractions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".