Bibliographic record
Abstract
This paper describes a laboratory testing program developed to assess the hydraulic properties of coarse rockfill by using a custom-built large-scale permeameter. Tests were performed by using samples 1.5 m in length and 0.7 m3 in volume. Knowing the hydraulic characteristics of coarse rockfill is important for assessing the safety of the structures under anticipated flow conditions in flow-through rockfill embankments. Flow in rockfill structures often departs from the laminar flow regime at typical operating flow conditions because of the characteristics of the rockfill materials. These characteristics include porosity, particle shape, particle size, roughness, and the tortuosity of the voids within the structure, which result in high velocities in large interconnected void spaces. For this reason, flow-through rockfill structures cannot be predicted by using Darcy’s law. The design and construction of a large-scale permeameter built at the University of Manitoba Hydraulic Research and Testing Facility is presented in detail. The experimental program, which used the large-scale permeameter, studied the nonlinear hydraulic characteristics of coarse rockfill materials for the coefficient in the power-law relationship between hydraulic gradient and bulk velocity. The results demonstrate that the large-scale permeameter successfully characterized the flow-through conditions of a variety of rockfill sizes and gradations under typical flow conditions. Results also allowed the determination of the coefficients to design flow-through rockfill dams for local rockfills.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".