On the use of empirical methods for assessment of filters in embankment dams
Why this work is in the frame
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Bibliographic record
Abstract
A database of 80 embankment dams has been compiled that includes 23 dams that are reported to have experienced some form of internal erosion. An assessment is made of the potential for seepage-induced internal instability of the filter zone in all dams, using five empirical criteria for shape analysis of the grain size distribution curve. Similarly, an assessment is made of the likelihood of core–filter incompatibility in all of the dams, using an empirical criterion for excessive erosion. These two attributes of a filter gradation, namely its potential for internal instability and its capacity for soil retention, are combined in a unified plot. Evaluation of the database reveals a correlation between the attributes of a filter gradation and deficiencies that are attributed to internal erosion. The finding implies the unified plot may serve as a preliminary screening tool in engineering practice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it