A method to determine the relative importance of geological parameters that control the hydraulic erodibility of rock
Bibliographic record
Abstract
The most common methods used to evaluate the potential hydraulic erosion of rock are index-based methods, which correlate the force of flowing water and the capacity of a rock to resist erosion. This capacity is evaluated using erodibility indices, which combine a set of specific geological parameters. Nonetheless, there exists no clear consensus in regard to the relative importance assigned to the geological parameters. Our study proposes (i) a review of the existing index-based methods used to evaluate the hydraulic erodibility of rock and (ii) a method to determine the relative importance of the geological parameters governing the erodibility of rock. The developed approach relies on a large dataset of case studies providing details of unlined spillways subjected to erosion. We demonstrate that the analysed geological parameters can be classified according to their relative importance – from highest to lowest – as follows: (1) joint shear strength, (2) nature of the potentially eroding surface, (3) rock block volume, (4) joint opening, (5) rock block's shape and orientation relative to flow direction and (6) the rock mass deformation module. This ordering of the relative importance of the geological parameters agrees largely with previously established orderings that were based on field observations.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".