Equivalent granular void ratio for characterization of Singapore's Old Alluvium
Bibliographic record
Abstract
Characterization of the Old Alluvium (OA) Formation in Singapore is difficult due to its natural heterogeneity. The OA formation was deposited by a braided river system, and it exhibits a wide range of engineering properties. Its strength and stiffness at a given depth, within a few metres distance horizontally, may differ by an order of magnitude, and this cannot be explained by differences in void ratio alone. The formation consists of sand, silt, and clay particles in varying proportions with no consistent relation with depth. Since the majority of OA is sand mixed with fines (silt and clay), the concept of granular void ratio, which treats silt and clay particles as void, was first introduced to try to characterize the formation. Though the concept of the granular void ratio was more useful than that of the void ratio, it could not take into consideration the different contributions that nonplastic silt fines and plastic clay fines make to the shear strength and thus was found to be inappropriate for a natural soil such as OA. To deal with this problem, the relative contribution of different kinds of fines is incorporated through the concept of equivalent granular void ratio. The merit of equivalent granular void ratio for the characterization of mixed soil formations is demonstrated in this paper through the analysis of results from triaxial tests on in situ OA samples.Key words: natural soil, clayey sand, Singapore Old Alluvium, heterogeneity, granular void ratio.
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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 | 0.003 | 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) | 0.000 | 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 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".