United void index for normalizing virgin compression of reconstituted clays
Bibliographic record
Abstract
The intrinsic compression framework that uses the void index for normalizing the virgin compression of reconstituted clays has been widely applied for academic and practical purposes. Past studies have shown that the data of void index are scattered when the stress is out of the range from 100 to 1000 kPa. In this study, the key cause responsible for the scatter problem in the existing intrinsic compression framework is identified. A united void index is introduced for normalizing the compression curves of reconstituted clays over a wide stress range starting from the remoulded yield stress to 1000 kPa. The normalized unique line is termed the unified normalized compression line (UNCL). Its constitutive equation is established in terms of the united void index versus the effective vertical stress. The uniqueness of the UNCL is validated based on independent data from the literature and the data from the research team. It is suggested that the UNCL should be directly measured from the virgin compression. In the case without conducting consolidation tests, the correlations between the intrinsic parameters in the UNCL’s equation and two physical parameters are proposed for indirectly determining the UNCL. The accuracy of the empirical correlations is investigated via the comparisons between the calculated intrinsic parameters and the measurements.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".