Model development for energy dissipation over gabion stepped weirs using GEP and GMDH techniques
Bibliographic record
Abstract
A gabion stepped weir is a permeable weir that consists of a gabion box filled with stone aggregates. It is cost-effective structure used for the dissipation of water flow energy from upstream to downstream. Gabion weir also allows the movement of aquatic life and transportation of physical and chemical substances present in water. In this study, energy dissipation in terms of inverse relative energy dissipation (IRED) over gabion stepped weir has been studied. It was observed that the existing predictor does not give a reasonable estimate for IRED. Therefore, the data have been reanalysed to develop a generalized regression equation for IRED. Generalized models using Gene Expression Programming (GEP) and Group Method of Data Handling (GMDH) were also developed. The predictions based on GMDH model (R = 0.979, E = 0.96 and RMSE = 0.314) were found more satisfactory than those given by traditional regression equations (R = 0.929, E = 0.91 and RMSE = 0.557) as well as those given by the GEP model (R = 0.959, E = 0.94 and RMSE = 0.476). Sensitivity analysis shows that the Froude number before the jump is the most important parameter for the prediction of IRED.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".