Manning's roughness coefficient determination in laboratory experiments using 2D modeling and automatic calibration
Bibliographic record
Abstract
Reliable experimental data are essential for choosing and validating numerical models. Although numerous data sets have been presented in the literature, few have been made widely available to the scientific community. Additionally, these experimental data sets have generally given little attention to the determination of Manning's roughness coefficients. This paper addresses these two issues. Three channel configurations are studied: a flatbed channel, a channel with a triangular sill and a channel with a triangular abutment. Three increasing permanent discharges are used for each configuration, leading to nine test cases. The Manning's coefficients are determined using three methods: the traditional step method, automatic calibration, via a 2D hydrodynamic model, considering theoretical value intervals and automatic calibration ignoring these intervals. The results show that automatic calibration with theoretical value intervals is advantageous compared to the step method. Automatic calibration ignoring theoretical intervals yields low errors but unphysical values; therefore, it is not recommended.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".