Extending the Kenney–Lau method to dam core soils of glacial till
Bibliographic record
Abstract
The Kenney–Lau method, which is used to assess the internal stability of granular soils, is stretched in engineering practice to include soils with fines. This strays beyond the method’s intended range and may introduce potential uncertainty in terms of validity. Herein, results are presented from the assessment of grain size curves from core construction data belonging to a large number of existing dams with core material composed of widely graded glacial till soils. Some have experienced internal erosion events, and others have not, and based on the benchmark of historic performance data of these dams, the validity of the Kenney–Lau method in terms of glacial tills is investigated. Only dams in the same filter coarseness range are studied in order to reduce the influence of the filter. By contrasting dams with documented internal erosion history against the application results of the method, it indicates that the Kenney–Lau method can be extended with caution to include glacial till cores if within the proposed fines content and finer fraction ranges.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".