Identification of Hydraulic Parameters Influencing the Hydraulic Erodibility of Spillway Flow Channels
Bibliographic record
Abstract
The rock mass erosion of dam spillways, a phenomenon involving the interaction between the hydraulic load of water and the capability of the rock mass to resist its destruction, remains a critical safety issue. The erosion resistance of a rock mass can be estimated through several erodibility indices, including those of Kirsten, Pells or Bollaert. Several indices have been developed to link rock resistance to the hydraulic parameters of water, i.e., the hydraulic load applied on a rock mass. The developed indices use the average flow velocity, the average shear stress on the bottom of the flow channel, the stress applied to the internal joints of fractured rock mass, the dynamic impulse force, and the power dissipation of water to represent the erosive force of water. From these indices, several methods of assessing hydraulic erosion have been developed, and all use the threshold line concept. Nonetheless, several uncertainties are associated with these methods. This paper presents and discusses the various means of calculating the erosive force of water as a hazard parameter for predicting potential rock erosion. The representativeness of these approaches is also discussed, and we clarify nuances associated with each method. We then provide guidelines for future research aimed at improving estimates of the erosive force of the water within spillway flow channels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".