A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu data
Bibliographic record
Abstract
Dynamic Cone Penetration Test (CPTu) profile offshore sediments by impact penetration. To exploit their results in full, the measured data are converted to obtain a quasi-static equivalent profile. Dynamic CPTu conversion requires calibrated correction models. Calibration is currently done by using paired (i.e., very close) quasi-static and dynamic tests. It is shown here that paired test data, which may be inconvenient to acquire offshore, are not strictly necessary to convert dynamic CPTu data. A new probabilistic methodology is proposed to call upon quasi-static results from a much wider area in the conversion procedure. Those results feed the prior distribution of a converted profile, within a Bayesian updating scheme where strain-rate coefficient and correction model error are also described by updated stochastic variables. The updating scheme is solved numerically using the Transitional Markov Chain Monte Carlo sampling algorithm. To avoid undue influence of local profile heterogeneity, the statistic treatment of the quasi-static CPTu data takes place in the frequency domain, using a discrete cosine transform. The new procedure is applied to a CPTu campaign offshore Nice (France): dynamic tests are converted with equal precision using quasi-static data acquired at distances orders of magnitude larger than what was previously employed.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".