Bibliographic record
Abstract
This paper presents the results and findings from several sets of experimental data on afflux around bridge waterways in overbank flow condition. The paper also investigates the accuracy, capability, and suitability of one-dimensional hydraulic river modelling software (HEC-RAS and ISIS) to model flow through bridge structure. To eliminate a scaling effect between the laboratory-scale experiments and model techniques, all experimental results were scaled up using an undistorted-scale model in which both the vertical and horizontal scale ratio was 100. A total of six methods of predicting afflux, the energy method, the momentum method, the water surface profile (WSPRO) method, Yarnell's method (in HEC-RAS), the U.S. Bureau of Public Roads (USBPR) method, and the arch bridge method (in ISIS) were compared with scaled up experimental results. The results for ISIS are significantly different from the measured data and the output from HEC-RAS. The energy method and the momentum method in HEC-RAS are the most accurate methods to compute the afflux around the bridge waterways.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".