A method to determine relevant geomechanical parameters for evaluating the hydraulic erodibility of rock
Bibliographic record
Abstract
Among the methods used for evaluating the potential hydraulic erodibility of rock, the most common methods are those based on the correlation between the force of flowing water and the capacity of a rock to resist erosion, such as Annandale's and Pells' methods. The capacity of a rock to resist erosion is evaluated based on erodibility indices that are determined from specific geomechanical parameters of a rock mass. These indices include unconfined compressive strength (UCS) of rock, rock block size, joint shear strength, a block's shape and orientation relative to the direction of flow, joint openings, and the nature of the surface to be potentially eroded. However, it is difficult to determine the relevant geomechanical parameters for evaluating the hydraulic erodibility of rock. The assessment of eroded unlined spillways of dams has shown that the capacity of a rock to resist erosion is not accurately evaluated. Using more than 100 case studies, we develop a method to determine the relevant geomechanical parameters for evaluating the hydraulic erodibility of rock in unlined spillways. The UCS of rock is found not to be a relevant parameter for evaluating the hydraulic erodibility of rock. On the other hand, we find that the use of three-dimensional (3D) block volume measurements, instead of the block size factor used in Annandale's method, improves the rock block size estimation. Furthermore, the parameter representing the effect of a rock block's shape and orientation relative to the direction of flow, as considered in Pells' method, is more accurate than the parameter adopted by Annandale's 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".