Bibliographic record
Abstract
Silt tailings (slimes) are difficult materials to test in that, like sands, it is extremely difficult to obtain undisturbed samples and subsequently re-establish them in a triaxial cell for element testing in a laboratory in anything approaching their in situ condition. Evaluation of silt tailing behaviour has to depend on in situ tests, and the piezocone (CPTu) in particular. However, CPTs in silt generate substantial excess pore pressure and there is no established methodology to evaluate the measured responses in terms of soil properties, as drained sand-based CPT interpretation is inapplicable. A case history of particularly loose silt tailings is reported in which the National Center for Earthquake Engineering Research (NCEER) liquefaction assessment method would lead to uncertainty in the liquefaction potential. However, the extremely high CPTu excess pore pressure ratio, Bq, and low dimensionless CPT resistance, Qp, at this site indicates liquefaction is likely occurring during pushing of the CPT. Detailed finite element simulations of the CPT using a critical state model provided an effective stress framework to evaluate the in situ state parameter of the silt from the measured CPT data. This framework shows that the group of dimensionless CPT variables Q(1 – Bq) + 1 is fundamental for the evaluation of undrained response during CPT sounding. And, despite the high silt content, the interpretation indicates that the tailings are indeed liquefiable.Key words: liquefaction, CPT, silt, finite element, critical state.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".