Shear wave velocity estimation from piezocone test data for eastern Canada sands (Quebec and Ontario) - extended version with appendices
Bibliographic record
Abstract
Different relations between shear wave velocity and parameters obtained from seismic cone penetration tests are evaluated against a data set collected at 107 sites along the St. Lawrence River Valley in Eastern Canada. Only sands or sand-like soils with a normalized SBT index Ic less than 2.60 and a normalized porewater pressure ratio Bq less than 0.10 are considered in this study. All investigated soils are approximately 7,000-12,000 years old and are of a marine, mainly deltaic, origin. Correlations established for Holocene sands systematically under-predict shear wave velocities determined for the tested soils while the opposite is observed for correlation developed for Pleistocene sands. Non-linear regressions and residual analyses conducted for the tested soils indicate that the best prediction model has a functional form incorporating the cone tip resistance, the sleeve friction resistance, and the effective vertical overburden stress. A good correlation is also obtained with only the cone tip resistance and depth. As a result, two new equations are presented specifically for sands in the St. Lawrence River Lowlands which allow for an estimation of shear wave velocity based on piezocone test data. This open-file report is an extended version of a paper presented at the 69th Canadian Geotechnical Conference in Vancouver (Perret et al., 2016).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".