Experimental and numerical studies for estimating coefficient of discharge of side compound weir
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Bibliographic record
Abstract
A sharp-crested side compound weir is a flow diversion structure provided on one or both side walls of a channel to divert water from the main channel. Compound sharp-crested weirs are widely used in irrigation, hydraulics, and environmental engineering. This article presents results of experimental and numerical studies conducted on sharp-crested side compound weirs in open channels. Owing to the complex mechanism of flow through a side compound weir it is difficult to establish a regression model to accurately predict the coefficient of discharge (C d ). In this study, an alternative approach to the conventional regression modelling in the form of artificial neural network (ANN) has been used to predict the values of C d . A network architecture with trained values of connection weights and biases is recommended to predict C d . The input to ANN model consists of grouped parameters pertaining to the ratio of weighted crest height to the length of the side compound weir ([Formula: see text]), the ratio of upstream depth to length of the side compound weir (Y 1 /L), and upstream Froude number (F 1 ). The results of the ANN model applied herein were found to be superior to those obtained through regression modelling by previous researchers. The sensitivity analysis of the ANN model shows that [Formula: see text] is the most important parameter for the estimation of C d ; followed by Y 1 /L and F 1 .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it