Bibliographic record
Abstract
The steady-state lines (SSLs) for sand–silt mixtures with various fines contents (0%, 5%, 10%, 15%, 20%, 30%, 50%, 70%, and 94%) were studied. It was indicated that the location of the SSL in the e–p′ space is different for each mixture, but the SSLs are parallel. In the e – ln p′ plot, the SSLs are similar for the mixtures with a fines content of less than the transitional fines content (TFC) when tested under drained and undrained conditions and the intergranular and interfine void ratios are used. The data diverge when the fines contents are equal to or greater than the TFC, even though the interfine void ratios are used. The results of the tests conducted under drained and undrained conditions produced a unique SSL in the p′–q space for each material. Different SSLs in the p′–q space were observed for the studied materials, and the friction angle at steady state varied in the range of 37.3°–42.2°. The study showed that the SSLs can be represented by one line in tests under drained conditions if the fines contents are less (0%–30%) than the TFC and the corrected intergranular void ratios are used. The lines can also be represented by one line for sand–silt mixtures with high fines contents (50%–94%) if the corrected interfine void ratios are used instead of void ratios. Key words: steady-state line, sand–silt mixtures, transitional fines content, drained and undrained triaxial tests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".