Correlations among some parameters of coarse-grained soils — the multivariate probability distribution model
Bibliographic record
Abstract
A multivariate probability distribution model for seven parameters of coarse-grained soils is constructed based on the SAND/7/2794 database that was compiled by the authors. It is shown that the multivariate probability distribution captures the correlation behaviors in the database among the seven parameters. This multivariate distribution model serves as a prior distribution model in the Bayesian analysis and can be updated into the posterior distribution of the design soil parameter when multivariate site-specific information is available. It is shown that this Bayesian analysis is conceptually similar to what is routinely carried out in practice, which utilizes information from comparable sites to supplement limited site-specific information. The resulting posterior distribution from Bayesian analysis merely combines different uncertainties associated with different sources of “correlated” information in a more consistent way. In this paper, the parameters for the posterior distribution of the design soil parameter are summarized into engineer-friendly tables ( Tables 9 and 10 ) so that engineers do not need to conduct the actual Bayesian analysis. Caution should be taken in extrapolating the results of this paper to cases that are not covered by SAND/7/2794, because the resulting posterior distribution can be misleading. This caveat applies to conventional regression equations as well.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".