Suffusion susceptibility investigation by energy-based method and statistical analysis
Bibliographic record
Abstract
Internal erosion is one of the main causes of instabilities within hydraulic earth structures. Four internal erosion processes can be distinguished, and this study deals with the process of suffusion, which corresponds to the coupled processes of detachment–transport–filtration of the soil’s fine fraction between the coarse fraction. Because of the great length of earth structures and the heterogeneities of soils, it is very difficult to characterize the suffusion susceptibility of the different soils. Nevertheless, a statistical analysis can be performed to optimize the experimental campaign. By using a dedicated erodimeter, an experimental program was set up to study suffusion susceptibility of 31 specimens of nonplastic and low-plasticity soils. The suffusion susceptibility is determined by the erosion resistance index, which relates the total loss of mass with the total energy expended by the seepage flow. Fourteen physical parameters are selected, and a multi-variate statistical analysis leads to a correlation between the erosion resistance index and all these parameters. A statistical analysis is performed to identify the main parameters and to focus on those that can easily be measured on existing structures. By distinguishing gap-graded and widely graded soils, two correlations are proposed to estimate the erosion resistance index.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".