Comparative study of coral sand and silica sand in creep under general stress states
Bibliographic record
Abstract
Coral sand has individual characteristics that differ from silica sand, such as creep behavior that is always attributed to particle crushing under high stress states. To understand the creep behavior of coral sand under general stress levels, three series of comparative triaxial tests relevant to the deviator stress, confining pressure, and relative density were performed on coral sand and silica sand creeping for more than 5 days. The volumetric, axial, and shear creeps of coral sand are considerably larger than those of silica sand, particularly under a relatively high confining pressure. The volumetric creep strain of coral sand was found to be contractive, but that of silica sand appeared dilative according to the creep time. This difference is not mainly governed by particle crushing in coral sand because the grain-size distribution prior to and after creep is similar. The grain skeletons were observed using a scanning electron microscope, finding that, independent of the grain size and shape, the coral grains include large amounts of cavities. The creep of coral sand under general stress conditions is mainly caused by particle interlocking, i.e., the angular regions of some particles interlock into the cavities of other particles due to particle rotation. This structuration is induced by breakage of asperities and voids during creep such as the local instability near cavities.
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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 | 0.001 | 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".