Large Permeameter for Continuing Erosion Filter Tests
Bibliographic record
Abstract
ABSTRACT The Continuing Erosion Filter (CEF) test and criteria were developed by Foster and Fell to assess severities of internal erosion of an existing embankment dam whose filter does not satisfy modern filter design criteria developed by Sherard and Dunnigan. Those criteria were developed from laboratory tests on materials that may not be directly applicable to the widely graded filter materials. To address this issue, a large diameter (300 mm) rigid-wall permeameter was constructed to assess the internal erosion of zoned embankment dams constructed of widely graded filter materials. The large permeameter allows for testing of embankment dam fill materials up to a maximum particle size of 37.5 mm. A commissioning test program was conducted to verify the test equipment and testing methodology used in this study. Two CEF test results from the test program are reported in this paper to demonstrate the novel features of the permeameter cell design and its test setup. These features include a split-cell design for ergonomic function, real-time outflow rate measurement, inflow and filter specimen water pressure measurements, and erosion loss measurement. A wet method to estimate erosion loss was proposed as an alternative to the traditional oven-drying method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".